Method and System for Using Artificial Intelligence to Onboard a User for an Exercise Plan

ABSTRACT

A method is disclosed for using an artificial intelligence engine to onboard a user for an exercise plan. The method includes generating a machine learning model trained to receive as input onboarding data associated with a user and an onboarding protocol and, based on the onboarding data and the onboarding protocol, output an exercise plan. While a user performs an exercise using the exercise device, the method includes receiving the onboarding data associated with the user. The method includes determining, by the machine learning model using the onboarding data and the onboarding protocol, a fitness level of the user. The onboarding protocol includes exercises with tiered difficulty levels, the onboarding protocol increases a difficulty level for a subsequent exercise when an exercise is completed, and based on a completion state of a last exercise performed, the fitness level is determined. The method includes selecting a difficulty level for each exercise.

CROSS-REFERENCES TO RELATED APPLICATIONS

This application is a continuation-in-part of and claims priority toU.S. application Ser. No. 16/869,954, filed May 8, 2020, titled “System,Method and Apparatus for Rehabilitation and Exercise”, which claimspriority to both U.S. Prov. Application No. 62/858,244, filed Jun. 6,2019, titled “System for Individualized Rehabilitation Using Load Cellsin Handles and Foot Plates and Providing Haptic Feedback to a User” andU.S. Prov. Application No. 62/846,434, filed May 10, 2019, titled“Exercise Machine”. The current application further claims priority toU.S. Prov. Application No. 63/168,175, filed Mar. 30, 2021, titled“System and Method for an Artificial Intelligence Engine That Uses aMulti-Disciplinary Data Source to Determine Comorbidity InformationPertaining to Users and to Generate Exercise Plans for Desired UserGoals”. All applications are hereby incorporated by reference in theirentirety for all purposes.

TECHNICAL FIELD

This disclosure relates to exercise machines. More specifically, thisdisclosure relates to a method and system for using artificialintelligence to onboard a user for an exercise plan.

BACKGROUND

Exercise and rehabilitation devices, such as an cycling machine andbalance equipment, are used to facilitate exercise, strength training,osteogenesis, and/or rehabilitation of a user. A user may perform anexercise (e.g., cycling, balancing, bench press, pull down, arm curl,etc.) using the osteogenic isometric exercise, rehabilitation, and/orstrength training equipment to improve osteogenesis, bone growth, bonedensity, muscular hypertrophy, flexibility, balance, coordination,reduce pain, decrease rehabilitation time, increase strength, or somecombination thereof. The isometric exercise, rehabilitation, and/orstrength training equipment may include moveable portions onto which theuser adds a load or balances. For example, to perform a cyclingexercise, the user may sit in a seat, place each of the user's feet on arespective pedal of an cycling machine, and push on the pedals with theuser's feet while each of the pedals rotate in a circular motion. Toperform a balancing exercise, the user may stand on a balance board andbalance on top of the balance board as it shifts in one or moredirections. The isometric exercise, rehabilitation, and/or strengthtraining equipment may include non-movable portions onto which the useradds load. For example, to perform a leg-press-style exercise, the usermay sit in a seat, place each of the user's feet on a respective footplate, and push on the feet plates with the user's feet while the footplates remain in the same position.

SUMMARY

Representative embodiments set forth herein disclose various techniquesfor an adjustment of exercise based on artificial intelligence, exerciseplan, and user feedback. As used herein, the terms “exercise apparatus,”“exercise device,” “electromechanical device,” “exercise machine,”“rehabilitation device,” “cycling machine” “balance board,” and“isometric exercise and rehabilitation assembly” may be usedinterchangeably. The terms “exercise apparatus,” “exercise device,”“electromechanical device,” “exercise machine,” “rehabilitation device,”“cycling machine” “balance board,” and “isometric exercise andrehabilitation assembly” may also refer to an osteogenic, strengthtraining, isometric exercise, and/or rehabilitation assembly.

In one embodiment, a method is disclosed for using an artificialintelligence engine to modify a resistance of one or more pedals of anexercise device. The method includes generating, by the artificialintelligence engine, a machine learning model trained to receive one ormore measurements as input, and outputting, based on the one or moremeasurements, a control instruction that causes the exercise device tomodify the resistance of the one or more pedals. The method includesreceiving the one or more measurements from a sensor associated with theone or more pedals of the exercise device, determining whether the oneor more measurements satisfy a trigger condition, and responsive todetermining that the one or more measurements satisfy the triggercondition, transmitting the control instruction to the exercise device.

In one embodiment, a method is disclosed for using an artificialintelligence engine to perform a control action. The control action isbased on one or more measurements from a wearable device. The methodincludes generating, by the artificial intelligence engine, a machinelearning model trained to receive the one or more measurements as input,and outputting, based on the one or more measurements, a controlinstruction that causes the control action to be performed. The methodincludes receiving the one or more measurements from the wearable devicebeing worn by a user, determining whether the one or more measurementsindicate, during an interval training session, that one or morecharacteristics of the user are within a desired target zone, andresponsive to determining that the one or more measurements indicate theone or more characteristics of the user are not within the desiredtarget zone during the interval training session, performing the controlaction.

In one embodiment, a method is disclosed for using an artificialintelligence engine to modify resistance of one or more pedals of anexercise device. The method includes generating, by the artificialintelligence engine, a machine learning model trained to receive one ormore measurements as input, and outputting, based on the one or moremeasurements, a control instruction that causes the exercise device tomodify, independently from each other, the resistance of the one or morepedals. The method includes, while a user performs an exercise using theexercise device, receiving the one or more measurements from the one ormore sensors associated with the one or more pedals of the exercisedevice, and determining, based on the one or more measurements, aquantifiable or qualitative modification to the resistance provided by apedal of the one or more pedals. In one embodiment, the resistanceprovided by another pedal of the one or more pedals is not modified. Themethod includes transmitting the control instruction to the exercisedevice to cause the resistance provided by the pedal to be modified.

In one embodiment, a method is disclosed for using an artificialintelligence engine to present a user interface capable of presentingthe progress of a user in one or more domains. The method includesgenerating, by the artificial intelligence engine, a machine learningmodel trained to receive one or more measurements as input, andoutputting, based on the one or more measurements, a user interface thatcauses one or more graphical elements to dynamically change position onthe user interface. The method includes, while a user performs anexercise using the exercise device, receiving the one or moremeasurements from the one or more sensors associated with the exercisedevice, and presenting, on a computing device associated with theexercise device, one or more sections of the user interface. The one ormore sections of the user interface may each be related to a separatedomain comprising the one or more domains and wherein, based on the oneor more measurements, each section may include the one or more graphicalelements placed.

In one embodiment, a method is disclosed for using an artificialintelligence engine to interact with a user of an exercise device duringan exercise session. The method includes generating, by the artificialintelligence engine, a machine learning model trained to receive data asinput, and based on the data, to provide an output. The method includes,while a user performs an exercise using the exercise device, receivingthe data from an input peripheral of a computing device associated withthe user, and based on the data being received from the inputperipheral, determining, via the machine learning model, the output suchthat control of an aspect of the exercise device is enabled.

In one embodiment, a method is disclosed for using an artificialintelligence engine to onboard a user for an exercise plan. The methodincludes generating, by the artificial intelligence engine, a machinelearning model trained to receive as input both onboarding dataassociated with a user and an onboarding protocol and, based on theonboarding data and the onboarding protocol, output an exercise plan.The method includes, while a user performs an exercise using theexercise device, receiving the onboarding data associated with the user.The method includes determining, by the machine learning model using theonboarding data and the onboarding protocol, a fitness level of theuser, wherein the onboarding protocol comprises exercises with tiereddifficulty levels, wherein the onboarding protocol increases adifficulty level for a subsequent exercise comprising the exercises whenthe user completes an exercise comprising the exercises, and, furtherwherein, based on a completion state of a last exercise performed by theuser, the fitness level of the user is determined. The method includes,by associating the difficulty level for each exercise with the fitnesslevel of the user, selecting a difficulty level for each exercisecomprising the exercise plan.

In one embodiment, a tangible, non-transitory computer-readable mediumstoring instructions that, when executed, cause a processing device toperform any of the operations of any of the methods disclosed herein.

In one embodiment, a system includes a memory device storinginstructions and a processing device communicatively coupled to thememory device. The processing device may execute the instructions toperform any of the operations of any of the methods disclosed herein.

Other technical features may be readily apparent to one skilled in theart from the following figures, descriptions, and claims.

BRIEF DESCRIPTION OF THE DRAWINGS

For a detailed description of example embodiments, reference will now bemade to the accompanying drawings in which:

FIG. 1 illustrates a high-level component diagram of an illustrativesystem architecture according to certain embodiments of this disclosure;

FIG. 2 illustrates an elevated perspective view of one embodiment of anisometric exercise and rehabilitation assembly;

FIG. 3 illustrates a perspective view of the isometric exercise andrehabilitation assembly;

FIG. 4 illustrates a side view of the isometric exercise andrehabilitation assembly;

FIG. 5 illustrates a side view of the isometric exercise andrehabilitation assembly with a user performing a leg-press-styleexercise;

FIG. 6 illustrates a side view of the isometric exercise andrehabilitation assembly with a user performing a chest-press-styleexercise;

FIG. 7 illustrates a side view of the isometric exercise andrehabilitation assembly with a user performing a core-pull-styleexercise;

FIG. 8 illustrates a side view of the isometric exercise andrehabilitation assembly with a user performing a suitcase-lift-styleexercise;

FIG. 9 illustrates four examples of load cells that can be used in theisometric exercise assembly;

FIG. 10 illustrates a side view of a second embodiment of the isometricexercise and rehabilitation assembly with the user performing achest-press-style exercise and a user interface presenting informationto the user;

FIG. 11 illustrates a side view of the second embodiment of theisometric exercise and rehabilitation assembly with a user performing asuitcase-lift-style exercise and a user interface presenting informationto the user;

FIG. 12 illustrates a side view of the second embodiment of theisometric exercise and rehabilitation assembly with a user performing anarm-curl-style exercise and a user interface presenting information tothe user;

FIG. 13 illustrates a side view of the second embodiment of theisometric exercise and rehabilitation assembly with a user performing aleg-press-style exercise and a user interface presenting information tothe user;

FIG. 14 illustrates a side view of a third embodiment of the isometricexercise and rehabilitation assembly with the user performing achest-press-style exercise and a user interface presenting informationto the user;

FIG. 15 illustrates a side view of the third embodiment of the isometricexercise and rehabilitation assembly with the user performing apull-down-style exercise and a user interface presenting information tothe user;

FIG. 16 illustrates a side view of the third embodiment of the isometricexercise and rehabilitation assembly with a user performing anarm-curl-style exercise and a user interface presenting information tothe user;

FIG. 17 illustrates a side view of the third embodiment of the isometricexercise and rehabilitation assembly with a user performing aleg-press-style exercise and a user interface presenting information tothe user;

FIG. 18 illustrates a side view of the third embodiment of the isometricexercise and rehabilitation assembly with a user performing asuitcase-lift-style exercise and a user interface presenting informationto the user;

FIG. 19 illustrates a perspective view of an exercise machine;

FIGS. 20A-B illustrate side views of the exercise machine;

FIG. 21 illustrates an example user interface for entering a level ofpain of a user;

FIG. 22 illustrates an example user interface presenting a pedal settingfor a session as determined by a machine learning model;

FIG. 23 illustrates an example user interface presenting an exercisesession determined for a user by a machine learning model;

FIG. 24 illustrates an example user interface presenting details of aparticular exercise for a user to perform;

FIG. 25 illustrates an example user interface presenting an incentive tothe user for completing a set;

FIG. 26 illustrates an example user interface that includes options forthe user to indicate whether an exercise is too easy or too hard;

FIG. 27A illustrates an example method for generating, using a machinelearning model, an exercise session for a user and causing a virtualcoach to provide instructions pertaining to the exercise session;

FIG. 27B illustrates an example data structure including a set ofexercises tagged by exercise level of a user;

FIG. 28 illustrates an example method for filtering a set of exercisesto obtain one or more exercises to include in an exercise session for auser;

FIG. 29 illustrates an example method for adjusting an exercise sessionbased on user feedback;

FIG. 30 illustrates an example method for selecting a persona for thevirtual coach;

FIG. 31 illustrates an example user interface presenting an indicationthat an exercise is complete and congratulates the user;

FIG. 32 illustrates an example computer system;

FIG. 33 illustrates an example user interface presenting a user profile;

FIG. 34 illustrates an example user interface for selecting a physicalactivity goal and a pain level;

FIG. 35 illustrates an example user interface presenting informationpertaining to a first exercise of a baseline fitness test;

FIG. 36 illustrates an example user interface presenting informationpertaining to a second exercise of a baseline fitness test;

FIG. 37 illustrates an example user interface presenting a generatedexercise plan for a user;

FIG. 38 illustrates an example user interface presenting informationpertaining to a user's comorbidities, week one of an exercise plan, andevidential source pertaining to the comorbidities and the exercise plan;

FIG. 39 illustrates an example user interface presenting informationpertaining to week two of an exercise plan for a user;

FIG. 40 illustrates an example user interface presenting informationpertaining to a target energy consumption metric for an exercise;

FIG. 41A-41E illustrates an example data source including informationpertaining to exercises and physical activity goals;

FIG. 42 illustrates an example method for generating an exercise planbased on a selected physical activity goal;

FIG. 43 illustrates an example method for selecting a multimedia clipfor a user based on data pertaining to the user;

FIG. 44 illustrates an example method for determining, using one or moremachine learning models, at least one comorbidity for a user;

FIG. 45 illustrates an example user interface presenting informationpertaining to a control instruction;

FIG. 46 illustrates an example method for transmitting a controlinstruction that causes resistance of one or more pedals to be modified;

FIG. 47 illustrates an example user interface presenting informationpertaining to a notification;

FIG. 48 illustrates an example method for transmitting a controlinstruction that causes a control action to be performed based on one ormore measurements from a wearable device;

FIG. 49 illustrates another example method for transmitting a controlinstruction that causes a control action to be performed based on one ormore measurements from a wearable device;

FIG. 50 illustrates an example method for independently controllingresistance provided by different pedals;

FIG. 51 illustrates an example method for modifying the resistanceprovided by one or more pedals based on a measured strengthcharacteristic level of a limb of user;

FIG. 52 illustrates an example user interface presenting respectivegraphical elements for different domains associated with a user;

FIG. 53 illustrates an example user interface presenting detailedinformation of a domain selected from FIG. 52;

FIG. 54 illustrates an example method for dynamically moving, based onone or more measurements associated with a user, graphical elements insections associated with domains;

FIG. 55 illustrates an example method for modifying an exercise planbased on adjustment made by a user;

FIG. 56 illustrates an example user interface presenting an animatedvirtual character, wherein, responsive to user input, the animatedvirtual character performs a movement;

FIG. 57 illustrates an example method for determining an output tocontrol an aspect of an exercise bike based on input received from theuser; and

FIG. 58 illustrates an example method for using an onboarding protocolto generate an exercise plan for a user.

NOTATION AND NOMENCLATURE

Various terms are used to refer to particular system components.Different entities may refer to a component by different names—thisdocument does not intend to distinguish between components that differin name but not function. In the following discussion and in the claims,the terms “including” and “comprising” are used in an open-endedfashion, and thus should be interpreted to mean “including, but notlimited to . . . .” Also, the term “couple” or “couples” is intended tomean either an indirect or direct connection. Thus, if a first devicecouples to a second device, that connection may be through a directconnection or through an indirect connection via other devices andconnections.

Various terms are used to refer to particular system components.Different entities may refer to a component by different names—thisdocument does not intend to distinguish between components that differin name but not function. In the following discussion and in the claims,the terms “including” and “comprising” are used in an open-endedfashion, and thus should be interpreted to mean “including, but notlimited to . . . .” Also, the term “couple” or “couples” is intended tomean either an indirect or direct connection. Thus, if a first devicecouples to a second device, that connection may be through a directconnection or through an indirect connection via other devices andconnections.

The terminology used herein is for the purpose of describing particularexample embodiments only, and is not intended to be limiting. As usedherein, the singular forms “a,” “an,” and “the” may be intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. The method steps, processes, and operations described hereinare not to be construed as necessarily requiring their performance inthe particular order discussed or illustrated, unless specificallyidentified as an order of performance. It is also to be understood thatadditional or alternative steps may be employed.

The terms first, second, third, etc. may be used herein to describevarious elements, components, regions, layers and/or sections; however,these elements, components, regions, layers and/or sections should notbe limited by these terms. These terms may be only used to distinguishone element, component, region, layer or section from another region,layer or section. Terms such as “first,” “second,” and other numericalterms, when used herein, do not imply a sequence or order unless clearlyindicated by the context. Thus, a first element, component, region,layer or section discussed below could be termed a second element,component, region, layer or section without departing from the teachingsof the example embodiments. The phrase “at least one of,” when used witha list of items, means that different combinations of one or more of thelisted items may be used, and only one item in the list may be needed.For example, “at least one of: A, B, and C” includes any of thefollowing combinations: A, B, C, A and B, A and C, B and C, and A and Band C. In another example, the phrase “one or more” when used with alist of items means there may be one item or any suitable number ofitems exceeding one.

Spatially relative terms, such as “inner,” “outer,” “beneath,” “below,”“lower,” “above,” “upper,” “top,” “bottom,” and the like, may be usedherein. These spatially relative terms can be used for ease ofdescription to describe one element's or feature's relationship toanother element(s) or feature(s) as illustrated in the figures. Thespatially relative terms may also be intended to encompass differentorientations of the device in use, or operation, in addition to theorientation depicted in the figures. For example, if the device in thefigures is turned over, elements described as “below” or “beneath” otherelements or features would then be oriented “above” the other elementsor features. Thus, the example term “below” can encompass both anorientation of above and below. The device may be otherwise oriented(rotated 90 degrees or at other orientations) and the spatially relativedescriptions used herein interpreted accordingly.

Moreover, various functions described below can be implemented orsupported by one or more computer programs, each of which is formed fromcomputer readable program code and embodied in a computer readablemedium. The terms “application” and “program” refer to one or morecomputer programs, software components, sets of instructions,procedures, functions, objects, classes, instances, related data, or aportion thereof adapted for implementation in a suitable computerreadable program code. The phrase “computer readable program code”includes any type of computer code, including source code, object code,and executable code. The phrase “computer readable medium” includes anytype of medium capable of being accessed by a computer, such as readonly memory (ROM), random access memory (RAM), a hard disk drive, acompact disc (CD), a digital video disc (DVD), solid state drives(SSDs), flash memory, or any other type of memory. A “non-transitory”computer readable medium excludes wired, wireless, optical, or othercommunication links that transport transitory electrical or othersignals. A non-transitory computer readable medium includes media wheredata can be permanently stored and media where data can be stored andlater overwritten, such as a rewritable optical disc or an erasablememory device.

The term “bone geometry” may refer to bone diameter, bone density, boneshape, bone cross-section, bone length, bone weight, or any suitablebone dimension(s) and/or measurement(s).

The term “empirical data” may refer to data obtained and/or derivedbased on observation, experience, measurement, and/or research.

The term “strain,” when used in context with a bone of a user, may referto an amount, proportion, or degree of deformation of the bone material.

The terms “exercise machine” and “isometric exercise and rehabilitationassembly” may be used interchangeably herein.

The terms “body part” and “body portion” may be used interchangeablyherein.

An exercise plan may include one or more exercise sessions. Eachexercise session may include one or more exercises of any type (e.g.,cycling, running, pull-ups, sit-ups, stretching, yoga, etc.). The one ormore exercises may include or be based on various specifications (e.g.,parameters, properties, values, attributes, etc.), such as a number ofrepetitions, a number of sets, a periodicity, a frequency, a difficultylevel, an amount of weight, a range of motion, a degree of flexion, adegree of extension, a skill level, or the like.

Definitions for other certain words and phrases are provided throughoutthis patent document. Those of ordinary skill in the art shouldunderstand that in many if not most instances, such definitions apply toprior as well as future uses of such defined words and phrases.

DETAILED DESCRIPTION

As typically healthy people grow from infants to children to adults,they experience bone growth. Such, growth, however, typically stops atapproximately age 30. After that point, without interventions asdescribed herein, bone loss (called osteoporosis), can start to occur.This does not mean that the body stops creating new bone. Rather, itmeans that the rate at which it creates new bone tends to slow, whilethe rate at which bone loss occurs tends to increase.

In addition, as people age and/or become less active than they oncewere, they may experience muscle loss. For example, muscles that are notused often may reduce in muscle mass. As a result, the muscles becomeweaker. In some instances, people may be affected by a disease, such asmuscular dystrophy, that causes the muscles to become progressivelyweaker and to have reduced muscle mass. To increase the muscle massand/or reduce the rate of muscle loss, people may exercise a muscle tocause muscular hypertrophy, thereby strengthening the muscle as themuscle grows. Muscular hypertrophy may refer to an increase in a size ofskeletal muscle through a growth in size of its component cells. Thereare two factors that contribute to muscular hypertrophy, (i)sarcoplasmic hypertrophy (increase in muscle glycogen storage), and (ii)myofibrillar hypertrophy (increase in myofibril size). The growth in thecells may be caused by an adaptive response that serves to increase anability to generate force or resist fatigue.

The rate at which such bone or muscle loss occurs generally acceleratesas people age. A net growth in bone can ultimately become a net loss inbone, longitudinally across time. By the time, in general, women areover 50 and men are over 70, net bone loss can reach a point wherebrittleness of the bones is so great that the risk of life-alteringfractures can occur. Examples of such fractures include fractures of thehip and femur. Of course, fractures can also occur due to participationin athletics or due to accidents. In such cases, it is just as relevantto have a need for bone growth which heals or speeds the healing of thefracture.

To understand why such fractures occur, it is useful to recognize thatbone is itself porous, with a somewhat-honeycomb like structure. Thisstructure may be dense and therefore stronger or it may be variegated,spread out and/or sparse, such latter structure being incapable ofcontinuously or continually supporting the weight (load) stressesexperienced in everyday living. When such loads exceed the supportcapability of the structure at a stressor point or points, a fractureoccurs. This is true whether the individual had a fragile bone structureor a strong one: it is a matter of physics, of the literal “breakingpoint.”

It is therefore preferable to have a means of mitigating or amelioratingbone loss and of healing fractures. Further, it is preferable toencourage new bone growth, thus increasing the density of the structuredescribed hereinabove. The increased bone density may increase theload-bearing capacities of the bone, thus making first or subsequentfractures less likely to occur. Reduced fractures may improve a qualityof life of the individual. The process of bone growth itself is referredto as osteogenesis, literally the creation of bone.

It is also preferable to have a means for mitigating or amelioratingmuscle mass loss and weakening of the muscles. Further, it is preferableto encourage muscle growth by increasing the muscle mass throughexercise. The increased muscle mass may enable a person to exert moreforce with the muscle and/or to resist fatigue in the muscle for alonger period of time.

In order to create new bone, at least three factors are necessary.First, the individual must have a sufficient intake of calcium, butsecond, in order to absorb that calcium, the individual must have asufficient intake and absorption of Vitamin D, a matter problematic forthose who have cystic fibrosis, who have undergone gastric bypasssurgery or have other absorption disorders or conditions which limitabsorption. Separately, supplemental estrogen for women and supplementaltestosterone for men can further ameliorate bone loss. On the otherhand, abuse of alcohol and smoking can harm one's bone structure.Medical conditions such as, without limitation, rheumatoid arthritis,renal disease, overactive parathyroid glands, diabetes or organtransplants can also exacerbate osteoporosis. Ethical pharmaceuticalssuch as, without limitation, hormone blockers, seizure medications andglucocorticoids are also capable of inducing such exacerbations. Buteven in the absence of medical conditions as described hereinabove,Vitamin D and calcium taken together do not create osteogenesis to adesirable degree or ameliorate bone loss to a desirable degree.

To achieve osteogenesis, therefore, one must add in the third factor:exercise. Specifically, one must subject one's bones to a force at leastequal to certain multiple of body weight, such multiples varyingdepending on the individual and the specific bone in question. As usedherein, “MOB” means Multiples of Body Weight. It has been determinedthrough research that subjecting a given bone to a certain threshold MOB(this may also be known as a “weight-bearing exercise”), even for anextremely short period of time, one simply sufficient to exceed thethreshold MOB, encourages and fosters osteogenesis in that bone.

Further, a person can achieve muscular hypertrophy by exercising themuscles for which increased muscle mass is desired. Strength trainingand/or resistance exercise may cause muscle tissue to increase. Forexample, pushing against or pulling on a stationary object with acertain amount of force may trigger the cells in the associated muscleto change and cause the muscle mass to increase.

In some embodiments disclosed herein, a control system for an exercisemachine is disclosed, not only capable of enabling an individual,preferably an older, less mobile individual or preferably an individualrecovering from a fracture, to engage easily in osteogenic exercisesand/or muscle strengthening exercises, but capable of usingpredetermined thresholds or dynamically calculating them, such that theperson using the machine can be immediately informed through real-timevisual and/or other sensorial feedback, that the osteogenic thresholdhas been exceeded, thus triggering osteogenesis for the subject bone (orbones), and/or that the muscular strength threshold has been exceeded,thereby triggering muscular hypertrophy for the subject muscle (ormuscles). The control system may be used to improve compliance with anexercise plan including one or more exercises.

The control system may receive one or more load measurements associatedwith forces exerted by both the left and right sides on left and rightportions (e.g., handles, foot plate or platform) of the exercise machineto enhance osteogenesis, bone growth, bone density improvement, and/ormuscle mass. The one or more load measurements may be a left loadmeasurement of a load added to a left load cell on a left portion of theexercise machine and a right load measurement of a load added to a rightload cell on a right portion of the exercise machine. A user interfacemay be provided by the control system that presents visualrepresentations of the separately measured left load and right loadwhere the respective left load and right load are added to therespective left load cell and right load cell at the subject portions ofthe exercise machine.

In some embodiments, initially, the control system may receive loadmeasurements via a data channel associated with each exercise of themachine. For example, there may be a data channel for a leg-press-styleexercise, a pull-down-style exercise, a suitcase-lift-style exercise, anarm-curl-style exercise, and so forth. Each data channel may include oneor more load cells (e.g., a left load cell and a right load cell) thatmeasure added load or applied force and transmit the load measurement tothe control system via its respective data channel. The control systemmay receive the load measurements from each of the data channels at afirst rate (e.g., 1 Hertz). If the control system detects a load from adata channel (e.g., hands resting on the handles including therespective load cells, or feet resting on the feet plate including therespective load cells), the control system may set that data channel asactive and start reading load measurements from that data channel at asecond rate (e.g., 10 Hertz) that is higher than the first rate.Further, the control system may set the other exercises associated withthe other data channels as inactive and stop reading load measurementsfrom the other data channels until the active exercise is complete. Theactive exercise may be complete when the one or more load measurementsreceived via the data channel exceed one or more target thresholds. Insome embodiments, the control system may determine an average loadmeasurement by accumulating raw load measurements over a certain periodof time (e.g., 5 seconds) and averaging the raw load measurements tosmooth the data (e.g., eliminates jumps or spikes in data) in an averageload measurement.

The control system may compare the one or more load measurements (e.g.,raw load measurements, or averaged load measurements) to one or moretarget thresholds. In some embodiments, a single load measurement may becompared to a single specific target threshold (e.g., a one-to-onerelationship). In some embodiments, a single load measurement may becompared to more than one specific target threshold (e.g., a one-to-manyrelationship). In some embodiments, more than one load measurement maybe compared to a single specific target threshold (e.g., a many-to-onerelationship). In some embodiments, more than one load measurement maybe compared to more than one specific target threshold (e.g., amany-to-many relationship).

The target thresholds may be an osteogenesis target threshold, amuscular strength target threshold, and/or a rehabilitation threshold.The osteogenesis target threshold may be determined based on a diseaseprotocol pertaining to the user, an age of the user, a gender of theuser, a sex of the user, a height of the user, a weight of the user, abone density of the user, etc. A disease protocol may refer to anyillness, disease, fracture, or ailment experienced by the user and anytreatment instructions provided by a caretaker for recovery and/orhealing. The disease protocol may also include a condition of healthwhere the goal is avoid a problem. The muscular strength targetthreshold may be determined based on a historical performance of theuser using the exercise machine (e.g., amount of pounds lifted for aparticular exercise, amount of force applied associated with each bodypart, etc.) and/or other exercise machines, a fitness level (e.g., howactive the user is) of the user, a diet of the user, a protocol fordetermining a muscular strength target, etc. The rehabilitation targetthreshold may be determined based on historical performance of the userusing the exercise machine (e.g., amount of force applied associatedwith each body part, speed of cycling, level of stability, etc.) and/orother exercise machines, a fitness level (e.g., how active the user is,the flexibility of the user, etc.) of the user, a diet of the user, anexercise plan for determining a rehabilitation target, the condition ofthe user (e.g., type of surgery the user underwent, the type of injurythe user sustained), physical characteristics of the user (e.g., an ageof the user, a gender of the user, a sex of the user, a height of theuser, a weight of the user, a bone density of the user), condition ofthe user's body part(s) (e.g., the pain level of a user), an exertionlevel of a user (e.g., how easy/hard the exercise session is for theuser), any other suitable characteristic, or combination thereof.

The control system may determine whether the one or more loadmeasurements exceed the one or more target thresholds. Responsive todetermining that the one or more load measurements exceed the one ormore target thresholds, the control system may cause a user interface topresent an indication that the one or more target thresholds have beenexceeded and an exercise is complete. Additionally, when the one or moretarget thresholds are exceeded, the control system may cause the userinterface to present an indication that instructs the user to applyadditional force (less than a safety limit) to attempt to set a personalmaximum record of weight lifted, pressed, pulled, or otherwise exertforce thereupon for that exercise.

Further, the user interface may present an indication when a loadmeasurement is approaching a target threshold for the user. In anotherexample, when the load measurement exceeds the target threshold, theuser interface may present an indication that the target threshold hasbeen exceeded, that the exercise is complete, and if there are anyremaining incomplete exercises in the exercise plan, that there isanother exercise to be completed by the user. If there are no remainingexercises in the exercise plan to complete, then the user interface maypresent an indication that all exercises in the exercise plan arecomplete and the user can rest. In addition, when the exercise plan iscomplete, the control system may generate a performance report thatpresents various information (e.g., charts and graphs of the right andleft load measurements received during each of the exercises, left andright maximum loads for the user received during each of the exercises,historical right and left load measurements received in the past,comparison of the current right and left load measurements with thehistorical right and left load measurement, an amount of pounds liftedor pressed that is determined based on the load measurements for each ofthe exercises, percent gained in load measurements over time, etc.).

Further, the one or more load measurements may each be compared to asafety limit. For example, a left load measurement and a right loadmeasurement may each be compared to the safety limit for the user. Thesafety limit may be determined for the user based on the user's diseaseprotocol. There may be different safety limits for different portions ofthe user's body on the left and the right side, one extremity versusanother extremity, a top portion of the user's body and a body portionof the user's body, etc., and for different exercises. For example, ifsomeone underwent left knee surgery, the safety limit for a user for aleft load measurement for a leg-press-style exercise may be differentfrom the safety limit for a right load measurement for that exercise anduser. If the safety limit is exceeded, an indication may be presented onthe user interface to instruct to reduce the amount of force the user isapplying and/or to instruct the user to stop applying force because thesafety limit is exceeded.

For those with any or all of the osteoporosis-exacerbating medicalconditions described herein, such a control system and exercise machinecan slow the rate of net bone loss by enabling osteogenesis to occurwithout exertions which would not be possible for someone whose healthis fragile, not robust. Another benefit of the present disclosure,therefore, is its ability to speed the healing of fractures inathletically robust individuals. Further, another benefit is theincrease in muscle mass by using the exercise machine to triggermuscular hypertrophy. The control system may provide an automatedinterface that improves compliance with an exercise plan by using areal-time feedback loop to measure loads added during each of theexercises, compare the load measurements to target thresholds and/orsafety limits that are uniquely determined for the user using theexercise machine, and provide various indications based on thecomparison. For example, the indications pertain to when the user shouldadd more load, when the target thresholds are exceeded, when the safetylimit is exceeded, when the exercise is complete, when the user shouldbegin another exercise, and so forth.

Bone Exercises and Their Benefits

The following exercises achieve bone strengthening results by exposingrelevant parts of a user to isometric forces which are selectedmultiples of body weight (MOB) of the user, a threshold level abovewhich bone mineral density increases. A MOB may be any fraction orrational number excluding zero. The specific MOB-multiple thresholdnecessary to effect such increases will naturally vary from individualto individual and may be more or less for any given individual.“Bone-strengthening,” as used herein, specifically includes, withoutlimitation, a process of osteogenesis, whether due to the creation ofnew bone as a result of an increase in the bone mineral density; orproximately to the introduction or causation of microfractures in theunderlying bone. The exercises referred to are as follows.

Leg Press

A leg-press-style exercise to improve isometric muscular strength in thefollowing key muscle groups: gluteals, hamstrings, quadriceps, spinalextensors and grip muscles as well as to increase resistance to skeletalfractures in leg bones such as the femur. In one example, theleg-press-style exercise can be performed approximately 4.2 MOB or moreof the user.

Chest Press

A chest-press-style exercise to improve isometric muscular strength inthe following key muscle groups: pectorals, deltoids, and tricep andgrip muscles as well as in increasing resistance to skeletal fracturesin the humerus, clavicle, radial, ulnar and rib pectoral regions. In oneexample, the chest-press-style exercise can be performed atapproximately 2.5 MOB or more of the user.

Suitcase Lift

A suitcase-lift-style exercise to improve isometric muscular strength inthe following key muscle groups: gluteals, hamstrings, quadriceps,spinal extensors, abdominals, and upper back and grip muscles as well asto increase resistance to skeletal fractures in the femur and spine. Inone example, the suitcase-lift-style exercise can be performed atapproximately 2.5 MOB or more of the user.

Arm Curl

An arm-curl-style exercise to improve isometric muscular strength in thefollowing key muscle groups: biceps, brachialis, brachioradialis, gripmuscles and trunk as well as in increasing resistance to skeletalfractures in the humerus, ribs and spine. In one example, thearm-curl-style exercise can be performed at approximately 1.5 MOB ormore of the user.

Core Pull

A core-pull-style exercise to improve isometric muscular strength in thefollowing key muscle groups: elbow flexors, grip muscles, latissimusdorsi, hip flexors and trunk as well as in increasing resistance toskeletal fractures in the ribs and spine. In one example, thecore-pull-style exercise can be performed at approximately 1.5 MOB ormore of the user.

Grip Strength

A grip-strengthening-style exercise which may preferably be situatedaround a station in an exercise machine, in order to improve strength inthe muscles of the hand and forearm. Grip strength is medically salientbecause it has been positively correlated with better states of health.

In some embodiments, a balance board may be communicatively coupled tothe control system. For example, the balance board may include a networkinterface that communicates with the control system via any suitableinterface protocol (e.g., Bluetooth, WiFi, cellular). The balance boardmay include pressure sensors and may obtain measurements of locationsand amount of pressure applied to the balance board. The measurementsmay be transmitted to the control system. The control system may presenta game or interactive exercise on a user interface. The game orinteractive exercise may modify screens or adjust graphics that aredisplayed based on the measurements received from the balance board. Thebalance board may be used by a user to perform any suitable type ofplank (e.g., knee plank, regular feet and elbow plank, table plank withelbows, or the like). Accordingly, the balance board may be configuredto be used with arms on the balance board, knees on the balance board,and/or feet standing on the balance board. The games or interactiveexercises may encourage the user during the game or interactiveexercises to increase compliance and neuro-motor control after asurgery, for example.

The exercise machine, balance board, wristband, goniometer, and/or anysuitable accessory may be used for various reasons in various markets.For example, users may use the exercise machine, balance board,wristband, goniometer, and/or any suitable accessory in the orthopedicmarket if the users suffer from chronic musculosketal pain (e.g., knees,hips, shoulders, and back). The exercise machine, balance board,wristband, goniometer, and/or any suitable accessory may be used to helpwith prehabilitation (prehab), as well as optimize post-surgicaloutcomes. Users may use the exercise machine, balance board, wristband,goniometer, and/or any suitable accessory in the back and neck painmarket if the users suffer with chronic back and neck pain and they wantto avoid surgery and experience long-term relief, as well as users thatare in recovery following surgery. Users may use the exercise machine,balance board, wristband, goniometer, and/or any suitable accessory inthe cardiovascular market if they desire to prevent or recover fromlife-threatening cardiovascular disease, especially heart attacks andstroke. Users may use the exercise machine, balance board, wristband,goniometer, and/or any suitable accessory in the neurological market ifthey desire to recover from stroke, or have conditions like Parkinson'sDisease and/or Multiple Sclerosis, and the users desire to achievebetter balance, strength, and muscle symmetry in order to slowprogression of the medical condition.

In some embodiments, bone growth, muscle growth, rehabilitation,prehabilitation, and the like may be needed to perform certain physicalactivities. For example, a person may require a certain amount of musclemass to move an object having a particular weight. While the physicalactivity may be desirable, some people may lack the appropriate bonemass, muscle mass, or physical ability in general to perform thephysical activity. In one example, a grandparent may desire to play withtheir grandchildren, and may want to select that physical activity as agoal. However, the grandparent may not be aware of what levels ofattainment are associated with the physical activity goal of playingwith their grandchildren. As used herein, levels of attainment may referto any physical, emotional, intellectual or other quality associatedwith such attainment, e.g., strength, endurance, balance, intelligence,neurological responsiveness, emotional well-being, range of motion, andmobility. A user may lack the proper knowledge, training, and/oreducation to determine which exercises to perform to target appropriatebody portions used, for example, to achieve the appropriate levels ofattainment to be able to play with their grandchildren. Further, anotherproblem that users may experience is the ability to determine when theuser may be at risk for having or developing various comorbitities in areal-time or near real-time manner. Such knowledge may be useful for auser to prevent the comorbidity from arising and/or to encourage orsuggest to a user to consult with a health professional to takepreventative care measures.

Accordingly, some embodiments of the present disclosure provide atechnical solution for enabling a user to select one or more physicalactivity goals they desire to achieve and for generating an improvedexercise plan that enables the user to achieve the one or more physicalactivity goals. The system may use an artificial intelligence engine togenerate machine learning models that use one or more curated,multi-disciplinary data sources to generate the improved exercise plan.A given data source may include associations between the selectedphysical activity goal and one or more levels of attainment pertainingto achieving the physical life goal, associations between the one ormore levels of attainment and one or more body portions, andassociations between the one or more body portions and one or moreexercises that target the one or more body portions. Using the datasource, the artificial intelligence engine may generate a machinelearning model to use the associations to generate improved exerciseplans. Further, a machine learning model may be trained to predict alength of time it will take a user, if they follow the improved exerciseplan, to achieve their physical activity life goal.

The levels of attainment may be objectively monitored and/or measuredusing various performance measurements from one or more sensors,characteristics of users of the exercise machine, user-reporteddifficulty levels of exercises, user-reported pain levels, and the like.An onboarding protocol may be used to establish a baseline describing afitness level of the user, and the fitness level of the user, in theimproved exercise plan, may be used to select difficulty levels ofexercises. A machine learning model may be trained to perform theonboarding protocol and to determine the fitness level of the user. Theimproved exercise plan may be dynamically updated based oncharacteristics of the user, selected physical activity levels,performance measurements, user-reported difficulties of the exercises,user-reported pain levels, and the like. In some embodiments, to complywith the exercise plan, the exercise machine may be controlled using asignal that indicates changing an attribute of an operating parameter ofthe exercise machine. The control system may change the attribute of theoperating parameter in response to receiving the signal.

In some embodiments, numerous enhanced user interfaces may be used toenable the user to create a profile, select physical activity goals,view generated improved exercise plans, perform exercises, view/listento multimedia regarding the exercises, provider user-reported feedback,view comorbidity information, view evidential trails for the comorbidityinformation and the exercise plans, control the exercise machine, andthe like. The user interfaces may present information in a beneficialmanner, especially on a small screen used by mobile devices (e.g.,smartphones, tablets), such that the user is presented with pertinentinformation without having to drill down into numerous other userinterfaces or to open up different applications or websites.Accordingly, the enhanced user interface may improve the user'sexperience using a computing device, thus providing a technicalimprovement to computing technology.

The following discussion is directed to various embodiments of thepresent disclosure. Although these embodiments are given as examples,the embodiments disclosed should not be interpreted, or otherwise used,as limiting the scope of the disclosure, including the claims. Inaddition, one of ordinary skill in the art will understand that thefollowing description has broad application, and the discussion of anyembodiment is meant only to be exemplary of that embodiment, and notintended to intimate that the scope of the disclosure, including theclaims, is limited to that embodiment.

FIG. 1 illustrates a high-level component diagram of an illustrativesystem architecture 10 according to certain embodiments of thisdisclosure. In some embodiments, the system architecture 10 may includea computing device 12 communicatively coupled to an exercise machine100. The computing device 12 may also be communicatively coupled with acomputing device 15 and a cloud-based computing system 16. As usedherein, a cloud-based computing system refers, without limitation, toany remote or distal computing system accessed over a network link. Eachof the computing device 12, computing device 15, and/or the exercisemachine 100 may include one or more processing devices, memory devices,and network interface devices. In some embodiments, the computing device12 may be included as part of the structure of the exercise machine 100.In some embodiments, the computing device 12 may be separate from theexercise machine 100. For example, the computing device 12 may be asmartphone, tablet, laptop, or the like.

The network interface devices may enable communication via a wirelessprotocol for transmitting data over short distances, such as Bluetooth,ZigBee, near field communication (NFC), etc. In some embodiments, thecomputing device 12 is communicatively coupled to the exercise machine100 via Bluetooth. Additionally, the network interface devices mayenable communicating data over long distances, and in one example, thecomputing device 12 may communicate with a network 20. Network 20 may bea public network (e.g., connected to the Internet via wired (Ethernet)or wireless (WiFi)), a private network (e.g., a local area network(LAN), wide area network (WAN), virtual private network (VPN)), or acombination thereof.

The computing device 12 may be any suitable computing device, such as alaptop, tablet, smartphone, or computer. The computing device 12 mayinclude a display that is capable of presenting a user interface 18 ofan application 17. The application 17 may be implemented in computerinstructions stored on the one or more memory devices of the computingdevice 12 and executable by the one or more processing devices of thecomputing device 12. The application 17 may be a stand-alone applicationthat is installed on the computing device 12 or may be an application(e.g., website) that executes via a web browser. The user interface 18may present various screens to a user that enable the user to login,enter personal information (e.g., health information; a disease protocolprescribed by a physician, trainer, or caretaker; age; gender; activitylevel; bone density; weight; height; patient measurements; etc.), viewan exercise plan, initiate an exercise in the exercise plan, view visualrepresentations of left load measurements and right load measurementsthat are received from left load cells and right load cells during theexercise, view a weight in pounds that are pushed, lifted, or pulledduring the exercise, view an indication when the user has almost reacheda target threshold, view an indication when the user has exceeded thetarget thresholds, view an indication when the user has set a newpersonal maximum for a load measurement and/or pounds pushed, lifted, orpulled, view an indication when a load measurement exceeds a safetylimit, view an indication to instruct the user to begin anotherexercise, view an indication that congratulates the user for completingall exercises in the exercise plan, and so forth, as described in moredetail below. The computing device 12 may also include instructionsstored on the one or more memory devices that, when executed by the oneor more processing devices of the computing device 12, performoperations to control the exercise machine 100.

The computing device 15 may execute an application 21. The application21 may be implemented in computer instructions stored on the one or morememory devices of the computing device 15 and executable by the one ormore processing devices of the computing device 15. The application 21may present a user interface 22 including various screens to aphysician, trainer, or caregiver that enable the person to create anexercise plan for a user based on a treatment (e.g., surgery, medicalprocedure, etc.) the user underwent and/or injury (e.g., sprain, tear,fracture, etc.) the user suffered, view progress of the user throughoutthe exercise plan, and/or view measured properties (e.g., force exertedon portions of the exercise machine 100) of the user during exercises ofthe exercise plan. The exercise plan specific to a patient may betransmitted via the network 20 to the cloud-based computing system 16for storage and/or to the computing device 12 so the patient may beginthe exercise plan. The exercise plan may specifying one or moreexercises that are available at the exercise machine 100.

The exercise machine 100 may be an osteogenic, muscular strengthening,isometric exercise and/or rehabilitation assembly. Solid state, static,or isometric exercise and rehabilitation equipment (e.g., exercisemachine 100) can be used to facilitate osteogenic exercises that areisometric in nature and/or to facilitate muscular strengtheningexercises. Such exercise and rehabilitation equipment can includeequipment in which there are no moving parts while the user isexercising. While there may be some flexing under load, incidentalmovement resulting from the tolerances of interlocking parts, and partsthat can move while performing adjustments on the exercise andrehabilitation equipment, these flexions and movements can comprise,without limitation, exercise and rehabilitation equipment from the fieldof isometric exercise and rehabilitation equipment.

The exercise machine 100 may include various load cells 110 disposed atvarious portions of the exercise machine 100. For example, one or moreleft load cells 110 may be located at one or more left feet plates orplatforms, and one or more right load cells may be located at one ormore right feet plates or platforms. Also, one or more left load cellsmay be located at one or more left handles, and one or more right loadcells may be located at one or more right handles. Each exercise in theexercise system may be associated with both a left and a right portion(e.g., handle or foot plate) of the exercise machine 100. For example, aleg-press-style exercise is associated with a left foot plate and aright foot plate. The left load cell at the left foot plate and theright load cell at the right foot plate may independently measure a loadadded onto the left foot plate and the right foot plate, respectively,and transmit the left load measurement and the right load measurement tothe computing device 12. The load added onto the load cells 110 mayrepresent an amount of weight added onto the load cells. In someembodiments, the load added onto the load cells 110 may represent anamount of force exerted by the user on the load cells. Accordingly, theleft load measurement and the right load measurement may be used topresent a left force (e.g., in Newtons) and a right force (e.g., inNewtons). The left force and right force may be totaled and convertedinto a total weight in pounds for the exercise. Each of the left force,the right force, and/or the total weight in pounds may be presented onthe user interface 18.

In some embodiments, the cloud-based computing system 16 may include oneor more servers 28 that form a distributed, grid, and/or peer-to-peer(P2P) computing architecture. Each of the servers 28 may include one ormore processing devices, memory devices, data storage, and/or networkinterface devices. The servers 28 may be in communication with oneanother via any suitable communication protocol. The servers 28 maystore profiles for each of the users that use the exercise machine 100.The profiles may include information about the users such as one or moredisease protocols, one or more exercise plans, a historical performance(e.g., loads applied to the left load cell and right load cell, totalweight in pounds, etc.) for each type of exercise that can be performedusing the exercise machine 100, health, age, race, credentials forlogging into the application 17, and so forth.

In some embodiments, the cloud-based computing system 16 may include atraining engine 50 and/or an artificial intelligence engine 65. Thecloud-based computing system 16 may include one or more servers 28 thatexecute the artificial intelligence engine 65 that uses one or moremachine learning models 60 to perform at least one of the embodimentsdisclosed herein. In some embodiments, the training engine 50 may beincluded as part of the artificial intelligence engine 65 and theartificial intelligence engine 65 may execute the training engine 50. Insome embodiments, the artificial intelligence engine 65 may use thetraining engine 50 to generate the one or more machine learning models60.

The artificial intelligence engine 65, the training engine 50, and/orthe one or more machine learning models 60 may be communicativelycoupled to the servers 28 or may be included in one of the servers 28.In some embodiments, the artificial intelligence engine 65, the trainingengine 50, and/or the machine learning models 60 may be included in thecomputing device 12.

The one or more of machine learning models 60 may refer to modelartifacts created, using training data that includes training inputs andcorresponding target outputs (correct answers for respective traininginputs), by the artificial intelligence engine 65 and/or the trainingengine 50. The training engine 50 may find patterns in the training datathat map the training input to the target output (the answer to bepredicted), and provide the machine learning models 60 that capturethese patterns. As described in more detail below, the set of machinelearning models 60 may be composed of, e.g., a single level of linear ornon-linear operations (e.g., a support vector machine (SVM)) or may be adeep network, i.e., a machine learning model composed of multiple levelsof non-linear operations. Examples of deep networks are neural networks,including convolutional neural networks, recurrent neural networks withone or more hidden layers, and fully connected neural networks.

In some embodiments, the training data may include various inputs (e.g.,a physical activity goal, range of motion of users, user-reported painlevel of users, user-reported difficulty levels of exercises, exerciseinformation, levels of attainment, characteristics of users (e.g., age,weight, height, gender, procedures performed, condition of user, goalsfor outcomes of exercising, etc.), performance measurements, and thelike) and mapped outputs. The mapped outputs may include an exerciseplan composed on various exercise sessions each including variousexercises, schedule of the exercise sessions, etc. In some embodiments,the training data may include other inputs (e.g., state of the exercisesession, exercise, exercise machine 100; progress of the user; events;characteristics of the user; measurements received from sensors, etc.)and other mapped outputs. The other mapped outputs may includecomorbidity information pertaining to the user. The other mapped outputsmay further include multimedia (e.g., video/audio) clips or segments fora virtual coach to speak, graphic images, video, and the like to bepresented on the user interface 18 of the computing device 12 before,during, or after the user performs the exercises. The virtual coach maybe implemented in computer instructions as part of application 17executing on the computing device 12. The virtual coach may be drivenand controlled by artificial intelligence (e.g., via one or more machinelearning models 60 ). For example, the machine learning model 60 may betrained to implement the virtual coach. Further, the training data mayinclude inputs pertaining to user feedback and/or progress of the userand outputs pertaining to a persona for the virtual coach to implement.The training data may include inputs of the progress of the user (e.g.,completion of an exercise) and output various incentives, rewards,and/or certificates. The training data may include inputs of theprogress of the user and/or the exercise plan and may outputnotifications pertaining to the progress and/or the exercise plan. Thetraining data may include inputs of user-reported pain levels,user-reported difficulty of exercises, difficulty levels of theexercises, etc. and may include mapped outputs of modifying the exerciseplan (e.g., removing an exercise, switching an exercise or to anotherexercise, adding an exercise, modifying an exercise session, adding anexercise session, removing an exercise session, etc.). The training datamay include one or more of measurements from sensors and/or ofcharacteristics of users and may further include mapped outputs ofcontrol instructions that modify operating parameters of the exercisedevice 100, as described further herein. Further, the training data mayinclude one or more user inputs and mapped outputs of a virtualcharacter to present on a user interface, as described further herein.Further, the training data may include one or more data outputs (e.g.,user feedback) and mapped outputs related to controlling an aspect ofthe exercise device 100. Further, the training data may include one ormore measurements and mapped outputs related to modifying, on a userinterface, various icons in a manner that positions or repositions thevarious icons relative to graphical elements that represent domainsassociated with an exercise plan, as described further herein. Further,the training data may include onboarding data and/or an onboardingprotocol, as well as mapped outputs of an exercise plan, as describedfurther herein. The machine learning model 60 may be trained using anyand/or all of the training data.

In some embodiments, the training engine 50 may train the machinelearning models 60 to output an exercise plan, wherein such plan mayinclude a schedule of exercise sessions and selected exercises for eachof the exercise sessions. Based on the inputs described herein, thetrained machine learning model 60 may select the exercises by filteringa set of exercises included in a tagged data structure (e.g., datasource). The machine learning model 60 may be trained to control thevirtual coach executing on the computing device 12. The machine learningmodel 60 may also be trained to provide incentives, rewards, and/orcertificates to the user. The machine learning model 60 may alsobetrained to modify the exercise plan and/or directly or indirectlycontrol the exercise machine 100 based on the progress of the userand/or feedback of the user (e.g., indications of a difficulty level ofan exercise). For example, if the user indicates an exercise is tooeasy, the machine learning model 60 may choose a new intensity for theexercise and the cloud-based computing system 16 may distally controlthe exercise machine 100 by increasing the intensity. Any suitablenumber of machine learning models 60 may be used. For example, separatemachine learning models 60 may be used for each respective functiondescribed above, and the machine learning models 60 may be linked suchthat the output from one machine learning model 60 may be input intoanother machine learning model 60.

The cloud-based computing system may include a data source 67 thatstores the training data for the training engine 50 and/or theartificial intelligence engine 65 to use to train the one or moremachine learning models 60. The data source may include exercises,physical activity goals, levels of attainment, body portions targeted byexercises, weights and/or parameters used to configure a prioritizationof certain levels of attainment throughout an exercise schedule,comorbidity information, health-related information, audio segments,video segments, motivational quotations, and so forth. The data source67 may include various tags and/or keys (e.g., primary, foreign, etc.)to associate items of the data with each other in the data source 67.The data source 67 may be a relational database, a pivot table, or anysuitable type of data structure configured to store data used for any ofthe operations described herein.

FIGS. 2-8 illustrates one or more embodiments of an osteogenic,isometric exercise and rehabilitation assembly 101. An aspect of thedisclosure includes an isometric exercise and rehabilitation assembly101. The assembly 101 can include a frame 102. The assembly 101 canfurther include one or more pairs of load handles 104, 106, 108 (e.g.,three shown) supported by the frame 102. Each load handle in one of thepairs of load handles 104, 106, 108 can be symmetrically spaced fromeach other relative to a vertical plane of the assembly 101. Forexample, the vertical plane can bisect the assembly 101 in alongitudinal direction.

During exercise, a user can grip and apply force to one of the pairs ofload handles 104, 106, 108. The term “apply force” can include a singleforce, more than one force, a range of forces, etc. and may be usedinterchangeably with “addition of load”. Each load handle in the pairsof load handles 104, 106, 108 can include at least one load cell 110 forseparately and independently measuring a force applied to, or a loadadded onto, respective load handles. Further, each foot plate 118 (e.g.,a left foot plate and a right foot plate) can include at least one loadcell 110 for separately and independently measuring a force applied to,or a load added onto, respective foot plates.

The placement of a load cell 110 in each pair of load handles 104, 106,108 and/or feet plates 118 can provide the ability to read variations inforce applied between the left and right sides of the user. This allowsa user or trainer to understand relative strength. This is also usefulin understanding strength when recovering from an injury.

In some embodiments, the assembly 101 further can include the computingdevice 12. One or more of the load cells 110 can be individually inelectrical communication with the computing device 12 either via a wiredor wireless connection. In some embodiments, the user interface 18presented via a display of the computing device 12 may indicate how toperform an exercise, how much force is being applied, a target force tobe applied, historical information for the user about how much forcethey applied at prior sessions, comparisons to averages, etc., as wellas additional information, recommendations, notifications, and/orindications described herein.

In some embodiments, the assembly 101 further includes a seat 112supported by the frame 102 in which a user sits while applying force tothe load handles and/or feet plates. In some embodiments, the seat 112can include a support such as a backboard 114. In some embodiments, theposition of the seat 112 is adjustable in a horizontal and/or verticaldimension. In some embodiments, the angle of the seat 112 is adjustable.In some embodiments, the angle of the backboard 114 is adjustable.Examples of how adjustments to the seat 112 and backboard 114 can beimplemented include, but are not limited to, using telescoping tubes andpins, hydraulic pistons, electric motors, etc. In some embodiments, theseat 112 can further include a fastening system 116 (FIG. 7), such as aseat belt, for securing the user to the seat 112.

In one example, the seat 112 can include a base 113 that is slidablymounted to a horizontal rail 111 of the frame 102. The seat 112 can beselectively repositionable and secured as indicated by the double-headedarrow. In another example, the seat 112 can include one or more supports117 (e.g., two shown) that are slidably mounted to a substantiallyvertical rail 115 of the frame 102. The seat 112 can be selectivelyrepositionable and secured as indicated by the double-headed arrow.

In some embodiments, a pair of feet plate 118 can be located angledtoward and in front of the seat 112. The user can apply force to thefeet plate 118 (FIG. 5) while sitting in the seat 112 during aleg-press-style exercise. The leg-press-style exercise can provide orenable osteogenesis, bone growth or bone density improvement for aportion of the skeletal system of the user. Further, the leg-press-styleexercise can provide or enable muscular hypertrophy for one or moremuscles of the user. In a leg-press-style exercise, the user can sit inthe seat 112, place their feet on respective feet plates 118, and pushon the pair of feet plate 118 using their legs.

In some embodiments, adjustments can be made to the position of the pairof feet plate 118. For example, these adjustments can include the heightof the pair of feet plate 118, the distance between the pair of feetplate 118 and the seat 112, the distance between each handle of the pairof feet plate 118, the angle of the pair of feet plate 118 relative tothe user, etc. In some embodiments, to account for natural differencesin limb length or injuries, each foot plate of the pair of feet plate118 can be adjusted separately.

In some embodiments, a first pair of load handles 104 can be locatedabove and in front of the seat 112. The user can apply force to the loadhandles 104 (FIG. 7) while being constrained in the seat 112 by thefastening system 116 in a core-pull-style exercise. The core-pull-styleexercise can provide or enable osteogenesis, bone growth or bone densityimprovement for a portion of the skeletal system of the user. Further,the core-pull-style exercise can provide or enable muscular hypertrophyfor one or more muscles of the user. In a core-pull-style exercise,while the lower body of the user is restrained from upward movement bythe fastening system 116, the user can sit in the seat 112, apply thefastening system 116, hold the first pair of load handles 104, and pullon the first pair of load handles 104 using their arms.

In some embodiments, adjustments can be made to the position of thefirst pair of load handles 104. For example, these adjustments caninclude the height of the first pair of load handles 104, the distancebetween the first pair of load handles 104 and the seat 112, thedistance between each handle of the first pair of load handles 104, theangle of the first load handles 104 relative to the user, etc. In someembodiments, to account for natural differences in limb length orinjuries, each handle of the first pair of load handles 104 can beadjusted separately.

In one example, the first pair of load handles 104 can include asub-frame 103 that is slidably mounted to a vertical rail 105 of theframe 102. The first pair of load handles 104 can be selectivelyrepositionable and secured as indicated by the double-headed arrow.

In some embodiments, a second pair of load handles 106 can be spacedapart from and in the front of the seat 112. While seated (FIG. 6), theuser can apply force to the second pair of load handles 106 in achest-press-style exercise. The chest-press-style exercise can provideor enable osteogenesis, bone growth or bone density improvement foranother portion of the skeletal system of the user. Further, thechest-press-style exercise can provide or enable muscular hypertrophyfor one or more muscles of the user. In a chest-press-style exercise,the user can sit in the seat 112, hold the second pair of load handles106, and push against the second pair of load handles 106 with theirarms.

In some embodiments, adjustments can be made to the position of thesecond pair of load handles 106. These adjustments can include theheight of the second pair of load handles 106, the distance between thesecond pair of load handles 106 and the seat 112, the distance betweeneach handle of the second pair of load handles 106, the angle of thesecond load handles 106 relative to the user, etc. In some embodiments,to account for natural differences in limb length or injuries, eachhandle of the second pair of load handles 106 can be adjustedseparately.

In one example, the second pair of load handles 106 can include thesub-frame 103 that is slidably mounted to the vertical rail 105 of theframe 102. The sub-frame 103 can be the same sub-frame 103 provided forthe first pair of load handles 104, or a different, independentsub-frame. The second pair of load handles 106 can be selectivelyrepositionable and secured as indicated by the double-headed arrow.

In some embodiments (FIG. 8), a third pair of load handles 108 can belocated immediately adjacent the seat 112, such that the user can standand apply force in a suitcase-lift-style exercise. Thesuitcase-lift-style exercise can provide or enable osteogenesis, bonegrowth or bone density improvement for still another portion of theskeletal system of the user. Further, the suitcase-lift-style exercisecan provide or enable muscular hypertrophy for one or more muscles ofthe user. Examples of the third pair of load handles 108 can extendhorizontally along a pair of respective axes that are parallel to thevertical plane. The third pair of load handles 108 can be horizontallyco-planar, such that a user can apply force to them in asuitcase-lift-style exercise. In the suitcase-lift-style exercise, theuser can stand on the floor or a horizontal portion of the frame 102,bend their knees, grip the third pair of load handles 108, and extendtheir legs to apply an upward force to the third pair of load handles108.

In some embodiments, adjustments can be made to the position of thethird pair of load handles 108. These adjustments can include the heightof the third pair of load handles 108, the distance between the thirdpair of load handles 108 and the seat 112, the distance between eachhandle of the third pair of load handles 108, the angle of the thirdload handles 108 relative to the user, etc. In some embodiments, toaccount for natural differences in limb length or injuries, each handleof the third pair of load handles 108 can be adjusted separately.

In one example, each load handle 108 of the third pair of load handles108 can include a sub-frame 109 that is slidably mounted in or to avertical tube 107 of the frame 102. Each load handle 108 of the thirdpair of load handles 108 can be selectively repositionable and securedas indicated by the double-headed arrows.

In other embodiments (not shown), the third pair of load handles 108 canbe reconfigured to be coaxial and located horizontally in front of theuser along an axis that is perpendicular to the vertical plane. The usercan apply force to the third pair of load handles 108 in adeadlift-style exercise. Like the suitcase-lift-style exercise, thedeadlift-style exercise can provide or enable osteogenesis, bone growthor bone density improvement for a portion of the skeletal system of theuser. Further, the deadlift-style exercise can provide or enablemuscular hypertrophy for one or more muscles of the user. In thedeadlift-style exercise, the user can stand on the floor or a horizontalportion of the frame 102, bend their knees, hold the third pair of loadhandles 108 in front of them, and extend their legs to apply an upwardforce to the third pair of load handles 108. In some embodiments, thethird pair of load handles 108 can be adjusted (e.g., rotated) from thedescribed coaxial position used for the deadlift-style exercise, to theparallel position (FIGS. 7, 8) used for the suitcase lift-styleexercise. The third pair of load handles 108, or others, can be used ina grip strengthening-style exercise to improve strength in the musclesof the hand and forearm.

FIG. 9 depicts several options for the load cells 110. In someembodiments, the load cells 110 can be piezoelectric load cells, such asPACEline CLP Piezoelectric Subminiature Load Washers. In otherembodiments, the load cells 110 can be hydraulic load cells, such asNOSHOK hydraulic load cells. In some versions, the load cells 110 caninclude strain gauges. Embodiments of the strain gauges can bebending-type strain gauges, such as Omega SGN-4/20-PN 4 mm grid, 20 ohmnickel foil resistors. Other examples of the strain gauges can bedouble-bending-type strain gauges 1202, such as Rudera Sensor RSL 642strain gauges. Still other embodiments of the strain gauges can behalf-bridge-type strain gauges 1204, such as Onyehn 4 pcs 50 kg HumanScale Load Cell Resistance Half-bridge/Amplifier Strain Weight Sensorswith 1 pcs HX 711 AD Weight Modules for Arduino DIY Electronic Scalestrain gauges. In some embodiments, the strain gauges can be S-typestrain gauges 1206, such as SENSORTRONICS S-TYPE LOAD CELL 60001 straingauges. Additionally, the strain gauges can be button-type strain gauges1208, such as Omega LCGB-250 250 lb Capacity Load Cells. Naturally, theload cells 110 can comprise combinations of these various examples. Theembodiments described herein are not limited to these examples.

FIGS. 10-13 illustrate views of a second embodiment of the isometricexercise and rehabilitation assembly 101. FIG. 10 illustrates a sideview of the second embodiment of the isometric exercise andrehabilitation assembly 101 with the user performing a chest-press-styleexercise and a user interface 18 presenting information to the user. Asdepicted, the user is the gripping second pair of load handles 106. Aleft load cell 110 and a right load cell 110 may be located at a leftload handle 106 and a right load handle 106, respectively, in the secondpair of load handles 106. The user may push on the second pair of loadhandles 106 to add load to the left load cell 110 and the right loadcell 110. The left load cell 110 may transmit a left load measurement tothe computing device 12, and the right load cell 110 may transmit aright load measurement to the computing device 12. The computing device12 may use the load measurements to provide various real-time feedbackon the user interface 18 as the user performs the chest-press-styleexercise.

In general, the user interface 18 may present real-time visual feedbackof the current load measurements or the current forces corresponding tothe load measurements, a weight in pounds associated with the loadmeasurements, incentive messages that encourage the user to exceedtarget thresholds (e.g., to trigger osteogenesis and/or muscularhypertrophy) and/or set personal records for maximum loads, historicalperformance of the user performing the exercise, and/or scripted promptsthat display images of one or more body portions indicating propertechnique for performing the exercise. The control system may providevarious multimedia (visual, audio), and/or haptic feedback to encouragethe user to exceed their target thresholds.

Initially, when the user has not added load onto any portion of theexercise machine 100 including one or more load cells 110, the computingdevice 12 may be operating in an idle mode. During the idle mode, thecomputing device 12 may be receiving load measurements at a firstfrequency from each data channel associated with an exercise. Forexample, there may be four data channels, one for each of achest-press-style exercise, a leg-press-style exercise, asuitcase-lift-style exercise, and a pulldown-style exercise. Althoughfour data channels are described for explanatory purposes, it should beunderstood that there may be any suitable number of data channels, where“any” refers to one or more. Each data channel may provide loadmeasurements to the computing device 12 from a respective left load celland a respective right load cell that are located at the portion of theexercise machine 100 where the user pushes or pulls for the respectiveexercises. The user interface 18 may present the load measurement fromeach left and right load cells (e.g., 8 load measurements for the 4 datachannels associated with the 4 exercises). Further, any targetthresholds and/or safety limits for the user performing the exercisesmay be presented on the user interface 18 during the idle mode. Forexample, a left target threshold, a right target load threshold, asafety limit, and/or a total weight target threshold for each of theexercises may be presented on the user interface 18 during the idlemode.

If the computing device 12 detects a minimum threshold amount of load(e.g., at least 10 pound-force (lbf)) added onto any of the load cells,the computing device switches from an idle mode to an exercise mode. Thedata channel including the load cell that sent the detected loadmeasurement may be set to active by the computing device 12. Further,the computing device 12 may set the other data channels to inactive andmay stop receiving load measurements from the load cells correspondingto the inactive data channels. The computing device 12 may begin readingdata from the load cells at the active data channel at a secondfrequency higher (e.g., high frequency data collection) than the firstfrequency when the computing device 12 was operating in the idle mode.Further, the user interface 18 may switch to presenting informationpertaining to the exercise associated with the active data channel andstop presenting information pertaining to the exercises associated withthe inactive data channels.

For example, the user may grip the second pair of handles 106 and applyforce. The computing device 12 may detect the load from the load cells110 located at the second pair of handles 106 and may set the datachannel associated with the chest-press-style exercise to active tobegin high frequency data collection from the load cells 110 via theactive data channel.

As depicted, the user interface 18 presents a left load measurement 1000as a left force and a right load measurement 1002 as a right force inreal-time or near real-time as the user is pressing on the second pairof handles 106. The values of the forces for the left load measurement1000 and the right load measurement 1002 are presented. There areseparate visual representations for the left load measurement 1000 andthe right load measurement 1002. In some embodiments, these loadmeasurements 1000 and 1002 may be represented in a bar char, line chart,graph, or any suitable visual representation. In some embodiments, aleft target threshold and a right target threshold for the user may bepresented on the user interface 18. In some embodiments, there may bemore than one left target threshold and more than one right targetthreshold. For example, the left target thresholds may relate to anosteogenesis target threshold determined using a user's disease protocoland/or a muscular strength target threshold determined using ahistorical performance of the user for a particular exercise. The righttarget thresholds may relate to an osteogenesis target thresholddetermined using a user's disease protocol and/or a muscular strengthtarget threshold determined using a historical performance of the userfor a particular exercise. For example, if the user fractured their leftarm and is rehabilitating the left arm, but the user's right arm ishealthy, the left osteogenesis target threshold may be different fromthe right osteogenesis target threshold.

If the left load measurement 1000 exceeds any of the left targetthresholds, an indication (e.g., starburst) may be presented on the userinterface 18 indicating that the particular left target threshold hasbeen exceeded and/or osteogenesis and/or muscular hypertrophy has beentriggered in one or more portions of the body. If the right loadmeasurement 1002 exceeds any of the right target thresholds, anindication (e.g., starburst) may be presented on the user interface 18indicating that the particular right target threshold has been exceededand/or osteogenesis and/or muscular hypertrophy has been triggered inanother portion of the body. Further, if either or both of the left andright target thresholds are exceeded, the indication may indicate thatthe exercise is complete and a congratulatory message may be presentedon the user interface 18. In some embodiments, another message may bepresented on the user interface 18 that encourages the user to continueadding load to set a new personal maximum left load measurement and/orright load measurement for the exercise.

In some embodiments, there may be a single target threshold to whichboth the left load measurement and the right load measurement arecompared. If either of the left or right load measurement exceed thesingle target threshold, the above-described indication may be presentedon the user interface 18.

In some embodiments, there may be a single safety limit to which theleft and right load measurements are compared. The single safety limitmay be determined based on the user's disease protocol (e.g., what typeof disease the user has, a severity of the disease, an age of the user,the height of the user, the weight of the user, what type of injury theuser sustained, what type of surgery the user underwent, the portion ofthe body affected by the disease, the exercise plan to rehabilitate theuser's body, instructions from a caregiver, etc.). If either or both ofthe left and right load measurements exceed the single safety limit, anindication may be presented on the user interface 18. The indication maywarn the user that the safety limit has been exceeded and recommend toreduce the amount of load added to the load cells 110 associated withthe exercise being performed by the user.

In some embodiments, more than one safety limit may be used. Forexample, if the user is rehabilitating a left leg, but a right leg ishealthy, there may be a left safety limit that is determined for theleft leg based on the user's disease protocol and there may be a rightsafety limit for the left leg determined based on the user's diseaseprotocol. The left load measurement may be compared to the left safetylimit, and the right load measurement may be compared to the rightsafety limit. If either or both the left load measurement and/or theright load measurement exceed the left safety limit and/or the rightsafety limit, respectively, an indication may be presented on the userinterface 18. The indication may warn the user that the respectivesafety limit has been exceeded and recommend to reduce the amount ofload added to the load cells 110 associated with the exercise beingperformed by the user.

Further, a total weight 1004 in pounds that is determined based on theleft and right load measurements is presented on the user interface 18.The total weight 1004 may dynamically change as the user adds load ontothe load cells 110. A target weight 1006 for the exercise for thecurrent day is also presented. This target weight 1006 may be determinedbased on the user's historical performance for the exercise. If thetotal weight 1004 exceeds the target weight 1006, an indication (e.g.,starburst) may be presented on the user interface 18 indicating thatosteogenesis and/or muscular hypertrophy has been triggered. Further,the indication may indicate that the exercise is complete and acongratulatory message may be presented on the user interface 18. Insome embodiments, another message may be presented on the user interface18 that encourages the user to continue adding load to set a newpersonal maximum record for the exercise.

Additionally, the user interface 18 may present a left grip strength1008 and a right grip strength 1010. In some embodiments, the left gripstrength and the right grip strength may be determined based on the leftload measurement and the right load measurement, respectively. Numericalvalues representing the left grip strength 1008 and the right gripstrength 1010 are displayed. Any suitable visual representation may beused to present the grip strengths (e.g., bar chart, line chart, etc.).The grip strengths may only be presented when the user is performing anexercise using handles.

The user interface 18 may also present a prompt 1012 that indicates thebody position the user should be in to perform the exercise, as well asindicate which body portions will be targeted by performing theexercise. The user interface 18 may present other current and historicalinformation related to the user performing the particular exercise. Forexample, the user interface 18 may present a visual representation 1014of the user's maximum weight lifted, pressed, pulled, or otherwiseexerted force for the day or a current exercise session. The userinterface 18 may present a visual representation 1016 of the user'sprevious maximum weight lifted, pressed, pulled, or otherwise exertedforce. The user interface 18 may present a visual representation 1018 ofthe user's maximum weight lifted, pressed, pulled, or otherwise exertedforce the first time the user performed the exercise. The user interface18 may present one or more visual representations 1020 for a weekly goalincluding how many sessions should be performed in the week and progressof the sessions as they are being performed. The user interface 18 maypresent a monthly goal including how many sessions should be performedin the month and progress of the sessions as they are being performed.Additional information and/or indications (e.g., incentivizing messages,recommendations, warnings, congratulatory messages, etc.) may bepresented on the user interface 18, as discussed further below.

FIG. 11 illustrates a side view of the second embodiment of theisometric exercise and rehabilitation assembly 101 with a userperforming a suitcase-lift-style exercise and the user interface 18presenting information to the user. The user interface 18 may presentsimilar types of information as discussed above with regards to FIG. 10,but the information in the user interface 18 in FIG. 11 may be tailoredfor the suit-case-lift-style exercise. That is, the data channel for thesuitcase-lift-style exercise may be set to active when the computingdevice 12 detects load measurements from load cells corresponding to thesuitcase-lift-style exercise, and the computing device 12 may presentthe various visual representations described with regards to FIG. 10 onthe user interface 18 in FIG. 11 based on at least the load measurementsfor the suitcase-lift-style exercise.

FIG. 12 illustrates a side view of the second embodiment of theisometric exercise and rehabilitation assembly 101 with a userperforming an arm-curl-style exercise and a user interface presentinginformation to the user. The user interface 18 may present similar typesinformation as discussed above with regards to FIG. 10, but theinformation in the user interface 18 in FIG. 12 may be tailored for thearm-curl-style exercise. That is, the data channel for thearm-curl-style exercise may be set to active when the computing device12 detects load measurements from load cells corresponding to thearm-curl-style exercise, and the computing device 12 may present thevarious visual representations described with regards to FIG. 10 on theuser interface 18 in FIG. 12 based on at least the load measurements forthe arm-curl-style exercise.

FIG. 13 illustrates a side view of the second embodiment of theisometric exercise and rehabilitation assembly 101 with a userperforming a leg-press-style exercise and a user interface presentinginformation to the user. The user interface 18 may present similar typesinformation as discussed above with regards to FIG. 10, but theinformation in the user interface 18 in FIG. 13 may be tailored for theleg-press-style exercise. That is, the data channel for theleg-press-style exercise may be set to active when the computing device12 detects load measurements from load cells corresponding to theleg-press-style exercise, and the computing device 12 may present thevarious visual representations described with regards to FIG. 10 on theuser interface 18 in FIG. 13 based on at least the load measurements forthe leg-press-style exercise.

FIGS. 14-18 illustrate views of a third embodiment of the isometricexercise and rehabilitation assembly 101. FIG. 14 illustrates a sideview of the third embodiment of the isometric exercise andrehabilitation assembly 100 with the user performing a chest-press-styleexercise and a user interface 18 presenting information to the user. Theuser interface 18 in FIG. 14 may present similar types of information asdiscussed above with regards to FIG. 10.

FIG. 15 illustrates a side view of the third embodiment of the isometricexercise and rehabilitation assembly 101 with the user performing apull-down-style exercise and a user interface 18 presenting informationto the user. The user interface 18 may present similar types ofinformation as discussed above with regards to FIG. 10, but theinformation in the user interface 18 in FIG. 15 may be tailored for thepull-down-style exercise. That is, the data channel for thepull-down-style exercise may be set to active when the computing device12 detects load measurements from load cells corresponding to thepull-down-style exercise, and the computing device 12 may present thevarious visual representations described with regards to FIG. 10 on theuser interface 18 in FIG. 15 based on at least the load measurements forthe pull-down-style exercise.

FIG. 16 illustrates a side view of the third embodiment of the isometricexercise and rehabilitation assembly 101 with a user performing anarm-curl-style exercise and a user interface 18 presenting informationto the user. The user interface 18 may present similar types ofinformation as discussed above with regards to FIG. 12.

FIG. 17 illustrates a side view of the third embodiment of the isometricexercise and rehabilitation assembly 101 with a user performing aleg-press-style exercise and a user interface 18 presenting informationto the user. The user interface 18 may present similar types ofinformation as discussed above with regards to FIG. 13.

FIG. 18 illustrates a side view of the third embodiment of the isometricexercise and rehabilitation assembly 101 with a user performing asuitcase-lift-style exercise and a user interface 18 presentinginformation to the user. The user interface 18 may present similar typesof information as discussed above with regards to FIG. 11.

After a person has an injury (e.g., sprain or fractured bone), a surgery(e.g., knee replacement), or a disease (e.g., muscular dystrophy), theperson's body is typically in a weakened state (e.g., physicallydisabled). Thus, clinicians, such as doctors and physical therapists,can prescribe exercise plans for rehabilitating their patients. Theexercises in these exercise plans help restore function, improvemobility, relieve pain, improve strength, improve flexibility, and,among other benefits, prevent or limit permanent physical disability inthe patients. Patients who follow their exercise plans typically showsigns of physical improvement and reduced pain at a faster the rate(i.e., a faster rate of recovery or rehabilitation).

In addition, after an injury or surgery, patients typically become lessactive than they once were, and they may experience muscle loss. Asexplained above, muscles that are not used often may reduce in musclemass and become weaker. To increase the muscle mass and/or reduce therate of muscle loss, people may conduct exercises according to anexercise plan.

Balancing and/or resistance exercise may cause muscle tissue toincrease. For example, balancing on a balance board or pushing andpulling on a stationary object (e.g., pedals of an exercise cycle) witha certain amount of force may trigger the cells in the associated muscleto change and cause the muscle mass to increase.

The subject matter disclosed herein relates to a control system for anexercise machine, not only capable of enabling an individual, preferablyan individual recovering from a fracture, an injury, or a surgery, toengage easily exercises according to an exercise plan, but capable ofusing predetermined thresholds or dynamically calculating them, suchthat the person using the exercise machine can be immediately informedthrough real-time visual and/or other sensorial feedback, that goals ofthe exercise plan have been met or exceeded, thus triggeringosteogenesis for the subject bone (or bones), and/or that the muscularstrength threshold has been exceeded, thereby triggering muscularhypertrophy for the subject muscle (or muscles). The control system maybe used to improve compliance with an exercise plan, whereby theexercise plan includes one or more exercises.

The control system may receive one or more measurements, such as loadmeasurements, associated with forces exerted by both the left and rightsides on left and right portions (e.g., pedals, base, or platform) ofthe exercise machine to enhance osteogenesis, bone growth, bone densityimprovement, stability, flexibility, range of motion, and/or musclemass. The one or more measurements (e.g., a load measurement) may be aleft measurement of a load or an increased resistance added to a leftload cell on a left portion of the exercise machine (e.g., a left pedalor a left portion of the platform) and a right measurement of a load oran increased resistance added to a right load cell on a right portion ofthe exercise machine (e.g., a right pedal or a right portion of theplatform). A user interface may be provided by the control system thatpresents visual representations of the separately measured left andright loads or resistances where the respective left and right load orresistances are added to the respective left and right load cells orsensors at the subject portions of the exercise machine. For example,the user interface may provide a video game that has an avatarrepresenting the user (e.g, the patient in rehabilitation). The avatarmay move in the video game and those moves may correlate with the movesof the patient. As the one or more measurements increase, the movementof the avatar may increase (e.g., if the video game is a car racingvideo game, as the patient increases the force exerted on the pedals,the speed of the avatar, in its car, will increase). Similarly, thecontrol system may receive one or more measurements associated withspeed, repetitions, balance, any other suitable measurement, orcombination thereof. Such measurements can be used to move the avatar.The measurements can be received from sensors coupled to the exercisemachine. For example, sensors can be coupled to the pedals of theexercise machine or to a base of the exercise machine.

In some embodiments, initially, the control system may determinemeasurements in accordance with an exercise plan associated with eachexercise of the video game. For example, there may be a first level ofthe video game that applies a first resistance to the pedals of theexercise machine (e.g., the cycle machine) and a second level of thevideo game that applies a second resistance to the pedals. Further, thecontrol system may receive measurements associated with each exercise asa patient is using the exercise machine. The control system may generatea target threshold in accordance with an exercise plan associated witheach exercise of the video game. For example, there may be a firstthreshold associated with the first level and a second thresholdassociated with the second level. The exercise may be complete when theone or more measurements are received and the one or more measurementsexceed one or more target thresholds. For example, if the patient isplaying the first level of the video game and one or more measurementsexceed a first target threshold, the first level may end and the controlsystem will select the level two for the patient to play. In someembodiments, the control system may determine an average measurement byaccumulating raw measurements over a certain period of time (e.g., 5seconds) and averaging the raw measurements to smooth the data (e.g.,eliminates jumps or spikes in data) in an average measurement.

The control system may compare the one or more measurements (e.g., rawmeasurements, or averaged measurements) to one or more targetthresholds. In some embodiments, a single measurement may be compared toa single specific target threshold (e.g., a one-to-one relationship). Insome embodiments, a single measurement may be compared to more than onespecific target threshold (e.g., a one-to-many relationship). In someembodiments, more than one measurement may be compared to a singlespecific target threshold (e.g., a many-to-one relationship). In someembodiments, more than one measurement may be compared to more than onespecific target threshold (e.g., a many-to-many relationship).

The target thresholds may be an osteogenesis target threshold, amuscular strength target threshold, a balance threshold, a speedthreshold, a range of motion threshold, a repetition threshold, anyother suitable threshold, or combination thereof. In addition to thethreshold explanations described above, the balance target threshold,the speed threshold, and/or the range of motion threshold may bedetermined based on a rehabilitation protocol pertaining to the user, anage of the user, a gender of the user, a sex of the user, a height ofthe user, a weight of the user, a bone density of the user, an injury ofthe user, a type of surgery of the user, a type of bone fracture of theuser, etc. A rehabilitation protocol may refer to any illness, disease,fracture, surgery, or ailment experienced by the user and any treatmentinstructions provided by a caretaker for recovery and/or healing. Therehabilitation protocol may also include a condition of health where thegoal is avoid a problem. Any of the target thresholds may be determinedbased on a historical performance of the user using the exercise machine(e.g., amount of pounds lifted for a particular exercise, amount offorce applied associated with each body part, the range of motion forpedaling, the level of exertion, the level of pain, etc.) and/or otherexercise machines, a fitness level (e.g., how active the user is) of theuser, a diet of the user, a protocol for determining a muscular strengthtarget, a range of motion target, etc.

The control system may determine whether the one or more measurementsexceed the one or more target thresholds. Responsive to determining thatthe one or more measurements exceed the one or more target thresholds,the control system may cause a user interface to present an indicationthat the one or more target thresholds have been met or exceeded and anexercise is complete. For example, the user has completed a level of thevideo game. Additionally, when the one or more target thresholds are metor exceeded, the control system may cause the user interface to presentan indication that instructs the user to apply additional force (lessthan a safety limit) to attempt to set a personal maximum record orachievement (e.g., of a rate of speed, of a level of stability, a numberof repetitions, of an amount of weight lifted, pressed, pulled, orotherwise exerted force) for that exercise. The control system may alsodetermine that one or more target thresholds (e.g., a level of pain oran exersion level) are met or exceeded and end the exercise game beingplayed. The control system may present the same game at an easierexercise game level or present a different game for the user to engagein different exercises to reduce the level of pain. In this way, theuser can continue exercising rather than stopping the rehabilitationsession due to pain. The video game may have one or more games, each ofwhich have one or more exercises that target one or more muscles groupsat one or more different levels of intensity.

Further, the user interface may present an indication when a measurementis approaching a target threshold for the user. In another example, whenthe measurement meets or exceeds the target threshold, the userinterface may present an indication that the target threshold has beenmet or exceeded, respectively, and that the exercise is complete. Thecontrol system may provide visual and/or audio encouragement and/orcoaching to the user during a video game. For example, as the user isnearing the target threshold, the control system may provide an audio ofa human voice encouraging the user to maintain or increase speed on thecycling machine to earn an achievement or reach the end of the exercisegame level. The control system may indicate if there are any remainingincomplete exercise game levels the video game as part of the exerciseplan, that there is another game or another level (e.g., with adifference exercise and/or goal) to be completed by the user. If thereare no remaining games or levels (i.e., exercises in the exercise plan)to complete, then the user interface may present an indication that allexercises in the exercise plan are complete and the user can rest. Inaddition, when the exercise plan is complete, the control system maygenerate a performance report that presents various information (e.g.,charts and graphs of the right and left measurements received duringeach of the exercises, left and right maximum loads for the userreceived during each of the exercises, historical right and leftmeasurements received in the past, comparison of the current right andleft measurements with the historical right and left measurement, anamount of pounds lifted or pressed that is determined based on themeasurements for each of the exercises, percent gained in measurementsover time, achievements earned, goals reached, exercise game levelscompleted, rankings as compared to a video game history of playing,etc.).

Further, the one or more measurements may each be compared to a safetylimit. For example, a left measurement and a right measurement may eachbe compared to the safety limit for the user. The safety limit may bedetermined for the user based on the user's disease protocol. There maybe different safety limits for different portions of the user's body onthe left and the right side, one extremity versus another extremity, atop portion of the user's body and a body portion of the user's body,etc., and for different exercises. For example, if someone underwentleft knee surgery, the safety limit for a user for a left measurementfor a cycling using a left leg may be different from the safety limitfor a right measurement for that exercise and user. If the safety limitis exceeded, an indication may be presented on the user interface toinstruct to reduce the amount of force or speed that the user isapplying and/or to instruct the user to stop applying force because thesafety limit has been exceeded.

Another benefit of the present disclosure is its ability to speed thehealing of fractures in athletically robust individuals. Further,another benefit is the increase in muscle mass by using the exercisemachine to trigger muscular hypertrophy. The control system may providean automated interface that improves compliance with an exercise plan byusing a real-time feedback loop to measure loads added during each ofthe exercises, (e.g. resistance applied to the pedals) compare themeasurements to target thresholds and/or safety limits that are uniquelydetermined for the user using the exercise machine, and provide variousindications based on the comparison. For example, the indicationspertain to when the user should add more load, when the targetthresholds are met or exceeded, when the safety limit is met orexceeded, when the exercise is complete, when the user should beginanother game, when the user should begin another level of the exercisegame, and so forth.

Rehabilitation Exercises and Their Benefits

The following exercises achieve rehabilitation results by exposingrelevant parts of a user to exercises that build strength, increaseflexibility, increase range of motion, increase balance, increasecoordination, decrease pain, decrease the amount of time required forrecovery, or any combination thereof. In addition to the exercisesmachines or devices described above in this disclosure, exercisemachines or devices used to facilitate the rehabilitation exercisesreferred to are as follows.

Cycling Machine

A cycling machine refers to a stationary bicycle used as exerciseequipment and/or rehabilitation equipment. The cycling machine includespedals configured to rotate. The cycling machine may include attachedhandlebars or may be used in combination with detached handlebars. Thecycling machine may include an attached seat or may be used incombination with a detached seat. The cycling machine can be used to forexercise targeted to improve the following key muscle groups: gluteals,hamstrings, quadriceps, thighs, adductors, abs, and grip muscles as wellas to increase flexibility, range of motion, and strength.

Balance Equipment

Balance equipment refers to an exercise machine or device, such as abalance board or a rocker device, for a user to stand on and maintainbalance and control as the balance board moves in various directions.The balance board can be used to for exercise targeted to can improvemobility, flexibility, proprioception, and strength in the following keymuscle groups: peroneals, gluteals, hamstrings, quadriceps, thighs,adductors, abs, and grip muscles as well as to increase flexibility,range of motion, and core strength.

The following discussion is directed to various embodiments of thepresent disclosure. Although these embodiments are given as examples,the embodiments disclosed should not be interpreted, or otherwise used,as limiting the scope of the disclosure, including the claims. Inaddition, one of ordinary skill in the art will understand that thefollowing description has broad application, and the discussion of anyembodiment is meant only to be exemplary of that embodiment, and notintended to intimate that the scope of the disclosure, including theclaims, is limited to that embodiment.

Exercise machines can include moving parts to provide dynamic exercisesto facilitate rehabilitation. A dynamic exercise can be, but is notlimited to an exercise where a user participates in an activity wherethe user moves and some resistance or load may be provided against themovement of the user. The FIGS. 19 and 20A-B illustrate embodiments ofan exercise machine 1900, generally shown, for use by a user forexercise. The exercise machine 1900 can be a stationary exercise machine(e.g., cycling machine) that can be used for exercise and/orrehabilitation. The exercise machine 1900 comprises a base 1902,generally indicated, that has front and rear sides 1904, 1906 and rightand left sides 1908, 1910. In the present embodiment of the base 1902,and as illustrated in the drawings, the rear side 1906 of the base 1902wider than the front side 1904. However, the base 1902 could be of anyshape. For example, the base 1902 could be rectangular, circular,rounded, trapezoidal, or square. In addition, in the present embodimentof the base 1902, the front side 1904 and the rear side 1906 can taper.For an individual who has limited mobility, the taper of the front side1904 and the rear side 1906 allows for ease of ingress and egress ontoand off of, respectively, the base 1902. However, the base 1902 couldhave raised rectangular edges, and the base 1902 may include a step foringress and egress onto and off of the base 1902. Slip pads 1912 can becoupled to the base 1902 adjacent each side to prevent slipping duringuse of the exercise machine 1900.

Embodiments of a first housing 1914, generally indicated, can be coupledto the base 1902. The first housing 1914 can be disposed adjacent to therear side 1906. A handlebar including one or more handles 1916 can becoupled to the first housing 1914. The handles 1916 can include grippads to prevent slipping during use of the exercise machine 1900.

The exercise machine 1900 comprises a multidimensional exercise controlsystem. The control system comprises a user interface 1918. The userinterface can be coupled to the first housing 1914. The user interface1918 may be or function as the user interface 18 in FIG. 1. Thecomputing device 12 may comprise the user interface 1918 and becommunicatively coupled to an exercise machine 100. The user interface1918 may also be communicatively coupled with the computing device 15and the cloud-based computing system 16. As used herein, a cloud-basedcomputing system refers, without limitation, to any remote or distalcomputing system accessed over a network link. Each of the userinterface 1918, computing device 15, and/or the exercise machine 1900may include one or more processing devices, memory devices, and networkinterface devices. In some embodiments, the user interface 1918 may beincluded as part of the structure of the exercise machine 1900. In someembodiments, the user interface 1918 may be separate from the exercisemachine 1900. For example, the user interface 1918 may be a smartphone,tablet, laptop, or the like. The computing device 12, the computingdevice 15, and/or the cloud-based computing system 16 can include memoryto store the application 17, such as one or more video games and/orexercise plans. The video game comprises one or more exercise games.Each exercise game may include one or more exercises that target one ormore parts or regions of a user's body. The parts or regions of eachexercise game may be the same, different, or overlap with other exercisegames. Each exercise game may include one or more levels. The levels mayinclude different levels of intensity of exercise for one or more bodyparts or regions of a user. The video game can be used for engagingusers to comply with an exercise plan, such as for rehabilitationpurposes.

Embodiments of a second housing 1920, generally indicated, can becoupled to the base 1902. The second housing 1920 can be disposedbetween the front and rear sides 1904, 1906. The second housing 1920 canbe disposed adjacent to and/or coupled to the first housing 1914. In thepresent embodiment of the second housing 1920, and as illustrated in thedrawings, the second housing 1920 is cylindrical shaped. However, thebase 1902 could be of any shape.

A wheel 1926 can be operatively coupled to the exercise machine 1900. Incertain embodiments, the exercise machine 1900 can have the wheel 1926coupled to the base 1902. The wheel 1926 can be a single wheel 1926, andthe wheel 1926 may be a flywheel. In certain embodiments, the exercisemachine 1900 can have a pair of wheels, and the wheels may be flywheels.The wheel 1926 can be disposed in the second housing 1920, and the wheel1926 can be independently rotatable about an axis. The wheel 1926 can bedisposed in in a cavity of the second housing 1920. The wheel 1926 canbe partially disposed in an openings of the second housing 1920. One ofskill in the art will appreciate that the wheel 1926 may be coupled tothe base 1902 by various means known in the art. As one example, asupport beam can extend from the base 1902 to a first axial, where anaxial extends along the axis. In this embodiment, the wheel 1926 can becoupled to and independently rotatable about the axial.

In some embodiments, a motor may be disposed in the second housing 1920and may be configured to be controlled by the computing device 12, thecomputing device 15, and/or the cloud-based computing system 16. Themotor may be configured to operate at a desired speed, which maydynamically modified by a control instruction. The motor may beelectrically coupled, physically coupled, and/or communicatively coupledto the wheel 1920, and may drive the wheel 1920 to rotate at a desiredrevolutions per minute, which may cause pedals 1922, 1924 to rotate ator near the desired revolutions per minute. The revolutions per minutemay be dynamically modified based on an attribute of an operatingparameter specified in a control instruction received from the computingdevice 12, the computing device 15, and/or the cloud-based computingsystem 16. Further, a resistance provided by pedals 1922, 1924 may bedynamically configured.

In some embodiments, the pedals 1922, 1924 may each be attached to apedal arm for rotation about an axle. In some embodiments, the pedals1922, 1924 may be movable on the pedal arms in order to adjust a rangeof motion used by a user in pedaling. For example, the pedals beinglocated inwardly toward the axle corresponds to a smaller range ofmotion than when the pedals are located outwardly away from the axle. Apressure sensor may be attached or embedded within each of the pedals1922, 1924 for measuring an amount of force applied by the user on thepedals 1922, 1924. The measurement from the sensor may be sent to thecloud-based computing system 16. The sensor may communicate wirelesslyto the computing device 12, the computing device 15, the cloud-basedcomputing system 16, the exercise machine 1900, or the like. The pedals1922, 1924 may be moved along the pedal arms based on operatingparameters provided in a control instruction generated by a machinelearning model 60. For example, a motor and/or actuator communicativelycoupled to the pedals 1922, 1924 may cause the pedals to move along thepedal arms to desired positions associated with desired range ofmotions.

A machine learning model 60 may be trained to receive input (e.g.,measurements) and to output a control instruction that causes anoperating parameter of the exercise device to change. In someembodiments, the operating parameter may represent or correspond to oneor more resistances provided by one or more pedals 1922, 1924; a rangeof motion of the one or more pedals 1922, 1924; a speed of the motor; arevolutions per minute of the wheel 1920; or some combination thereof.It should be noted that the operating parameters may be controlledindependently (e.g., a first pedal may be set to a first range of motionand a second pedal may be set to a second range of motion) or theoperating parameters may be similarly controlled (e.g., each of thefirst and second pedals is set to the same range of motion).

In some embodiments, pair of pedals (e.g., a right pedal 1922 and a leftpedal 1924 ) can be coupled to and extend from the wheel 1926. Thepedals 1922, 1924 can be configured to be engaged by the user, and thepedals 1922, 1924 can facilitate rotation of the respective wheel 1926.The pedals 1922, 1924 can be movably coupled to the wheel 1926. Morespecifically, the pedals 1922, 1924 can be adjusted radially by the userto various positions to accommodate the needs of the user. During use ofthe exercise machine 1900, the user can sit in a seat 1930 and engagethe pedals 1922, 1924. The seat 1930 may be detached from the exercisemachine 1900. In some embodiments, the seat 1930 may be attached to theexercise machine 1900. It should be readily appreciated that the usermay adjust the seat 1930 and/or the pedals 1922, 1924 to a desiredposition to accommodate the needs of the user for exercise orrehabilitation. When the user engages the pedals 1922, 1924, the usermay apply a force to respective pedals 1922, 1924 to engage and causerotation of a respective wheel 1926. By engaging respective pedals 1922,1924 and applying a force to the same, the user, to support osteogenesisand/or increase a range of motion of a user's legs, engages variousmuscles to push the respective pedals 1922, 1924. The pedals 1922, 1924may have straps or engagements for a user to engage with and pull thepedals 1922, 1924. Pulling the pedals 1922, 1924 may aid in the strengthand rehabilitation of additional muscles. A sensor 1934 can be coupledto the right pedal 1922. An additional sensor 1936 can be coupled to theleft pedal 1924. As described above, the sensors 1934, 1936 can beconfigured to collect sensor data correlating to the respective pedals1922, 1924. The sensors 1934, 1936 can be a Bluetooth sensor, a loadsensor, accelerometers, gyroscopes, magnetometers, any other suitablesensor, or combination thereof.

To further support osteogenesis during use of the exercise machine 1900by a user, the exercise machine 1900 can include a first resistancemechanism (not shown). The resistance mechanism can be coupled to thebase 1902, and the resistance mechanism can be disposed in the secondhousing 1920 adjacent to the wheel 1926. When the pedal 1922, 1924 areengaged by the user, the resistance mechanism can be configured toresist rotation of the wheel 1926. The resistance mechanisms may resistrotation of the wheel 1926 by any means known in the art.

It is to be appreciated that the exercise machine 1900 could comprise amotor coupled to each of the wheel 1926 and each motor is configured toaffect or regulate the independent rotation of a respective wheel 1926.Moreover, the motor 1928 affects or regulates the independent rotationof the wheel 1926 by engaging the wheel 1926 and selectively causing orresisting rotation of the wheel 1926. The motor 1928 can engage thewheel 1926 by any means known in the art. In one example, the motor 1928could engage gears to cause rotation of the wheel 1926. It is to beappreciated that the motor 1928 can operate congruently with orindependently of the resistance mechanisms to affect or regulate therotation of the wheel 1926. In certain embodiments, the motor 1928 cancause rotation of the wheel 1926, and the motor 1928 can resist rotationof the wheel 1926. In other embodiments with the motor 1928 and theresistance mechanism, the motor 1928 can rotate the wheel 1926 and theresistance mechanism can resist or stop rotation of the wheel 1926 whenthe motor 1928 stops rotating the wheel 1926. For regulating oraffecting the rotation of the wheel 1926, the present disclosure allowsfor many variations and combinations of the motor 1928 and theresistance mechanism.

During use of the exercise machine 1900 by a user, when the user appliesa force to the pedals 1922, 1924, the control system can maintain aconstant rotational velocity between each of the wheel 1926.Alternatively, the wheel 1926 can be mechanically interconnected. Forexample, the wheel 1926 could be mechanically interconnected by a chain,belt, gear system, or any other means to maintain a constant rotationalvelocity between the wheel 1926.

In a further embodiment of the exercise machine 1900, a control systemcan be coupled to an actuator, and the control system can be configuredto control the actuator. Moreover, the control system can be configuredto independently vary the resistance to each of the wheel 1926 tomaintain a select rotational velocity thereof, and to independently stoprotation of the wheel 1926. More specifically, the control system cancontrol the actuator to activate the resistance mechanism toindependently vary the resistance of the wheel 1926. In certainembodiments, the control system can be coupled to the motor 1928, andthe control system can be configured to control the motor 1928.Additionally, the control system can be configured to independentlymaintain select rotational velocities of the wheel 1926, and toindependently stop rotation of the wheel 1926. More specifically, thecontrol system can control the motor 1928 to independently maintainselect rotational velocities of the wheel 1926 by rotating, resisting,or stopping rotation of the wheel 1926. It is to be appreciated that thecontrol system may control the actuator and/or the motor 1928simultaneously or independently to maintain the select rotationalvelocities of the wheel 1926. For communicating the rotationalvelocities or accelerations of the wheel 1926 to the control system, thecontrol system may also include sensors located on the user or coupledto the wheel 1926. With the rotational velocities or accelerationsreceived from the sensors, the control system can determine, with aprocessor of the control system, a select rotational velocity of thewheel 1926. The control system can then control the motor 1928 and/orthe actuator to maintain the select rotational velocities of the wheel1926.

In some embodiment of the exercise machine 1900, a switch, notillustrated, can be disposed on the first housing 1914 for activatingthe control system. In another embodiment, a button, not illustrated,may be disposed on the first housing 1914 for activating the controlsystem. In yet another embodiment, a display 1932 of a user interface1918, such as a computer screen, iPad, or like device, can be coupled tothe exercise machine 1900 to activate the control system. The switch,display 1932, and/or button may be coupled to the exercise machine 1900by alternative or other means. For example, the switch, display 1932,and/or button could be coupled to the handle 1916. It is further to beappreciated that alternative means could be used to activate the controlsystem and the use of the switch, display 1932, or the button, is notmeant to be limiting.

In another embodiment, one or more biometric sensors, not shown, may becoupled to the exercise machine 1900 for activating the control system.The biometric sensor could be for, inter alia, detection, recognition,validation and/or analysis of data relating to: facial characteristics;a fingerprint, hand, eye (iris), or voice signature; DNA; and/orhandwriting. In yet another embodiment, the biometric sensor cancomprise position sensors located on the user. In addition, it iscontemplated that advancements of such biometric sensors may result inalternative sensors that could be incorporated in the exercise machine1900, i.e., biometric type sensors not currently on the market may beutilized. Further, the one or more biometric sensors may comprise abiometric system, which may be standalone or integrated.

In one embodiment, adjustment of exercise based on artificialintelligence, exercise plan, and user feedback is disclosed. An exerciseplan may include one or more exercise sessions. For example, an exerciseplan may include a schedule of a certain number of exercises sessionsfor a certain time period (e.g., 3 exercise sessions each week for 4weeks) that, if performed by the user, should result in a desiredoutcome (e.g., rehabilitation of a body part, strengthen a muscle,etc.). The exercise session may include one or more exercises forvarious sections (e.g., warm up, strength, flexibility, cycling, cooldown, etc.) The exercise plan may be generated using artificialintelligence via one or more trained machine learning models asdescribed herein. The exercise plan may include a plan of one or moreexercise sessions including exercises for a patient for rehabilitating abody part. The exercise plan may include exercises for one or moremuscle groups. The exercise plan may be generated by artificialintelligence and/or prescribed by a doctor, a physical therapist, or anyother qualified clinician.

For example, a machine learning model may be trained to select one ormore exercises for an exercise session based on various inputs. Theinputs may include the pain level of the user, the range of motion ofthe user, and/or characteristics of the user. These inputs may be usedto determine an exercise level of the user. The machine learning modelmay receive the exercise level as input and select correspondingexercises from a data structure by matching the exercise level of theuser to exercises having a tagged corresponding user exercise level.Various other techniques may be used to select the exercises for theexercise session.

The machine learning models may be trained to control a virtual coachexecuting on a computing device associated with the exercise machine100. The virtual coach may speak via a speaker of the computing device,may be a virtual avatar displayed on the user interface 18 of thecomputing device 12, may cause one or more messages, emails, text,notifications, prompts, etc. to be presented on the user interface 18.The virtual coach may perform actions based on various information, suchas progress of the user performing an exercise, the exercise plandetails, user feedback, and the like. For example, the virtual coach mayprovide encouragement to the user based on the progress of the userduring an exercise. The virtual coach may provide incentives, rewards,and/or certificates to the user as the user completes exercises. Thevirtual coach may have a particular persona that is selected for aparticular user. For example, some users may respond better and performexercises completely in response to a nice and encouraging persona forthe virtual coach, while other users may respond better to a moredemanding and strict (e.g., drill sergeant) persona.

By tailoring the exercise plan for the specific user and dynamicallyadjusting it using artificial intelligence, compliance with the exerciseplan may be enhanced. Further, the user may achieve their desired goalfaster by using the generated exercise plan because it is based on theirprogress and feedback (e.g., pain level, exercise difficulty level). Byachieving the desired outcome faster, computing resources (e.g.,processing, memory, network, etc.) may be reduced because the exercisemachine 100, the computing device 12, and/or the cloud-based computingsystem 16 may not have to continuously update the exercise plan.

Further, the virtual coach may provide a companion type of feel for theuser, which may further cause the user to comply with the exercise planmore efficiently and completely, thereby achieving their desired outcomefaster. The virtual coach may improve the user experience of using thecomputing device 12 and/or the exercise machine 100 because the personamay be selected specifically for the particular user. In some instances,the user may form a bond with the persona of the virtual coach if thepersona matches a friend in real life, family member, a significantother, or the like, and the bond may cause the user to feel a desire towant to listen to the virtual coach and/or complete the exercise plansuch that they don't let the virtual coach down. Such a situation mayalso save computing resources because the exercise plan may not have tobe adjusted and lengthened by adding additional exercise sessions.

As a result, various technical benefits may be achieved by the disclosedembodiments, as described above. Further, the user experience of usingthe exercise machine 100, the computing device 12, or both may beimproved based on the disclosed techniques due to exercising with thevirtual coach, the incentives, the rewards, the certificates, and thelike.

The processing device may be configured to execute the instructions toreceive user input data. As illustrated in FIG. 21, the user interface2100 may display a screen 2102 requesting a user to provide a painlevel. The user can select the level of pain (e.g., no pain, mild,moderate, severe, very severe) before an exercise begins. The level ofpain may be used to determine an exercise level of the user. Once thelevel of pain is selected, a testing phase may be initiated where a userperforms an exercise to determine their range of motion, for example.The level of pain and the range of motion may be used to determine theexercise level of the user. The user interface 2100 may provide visualand/or audio prompts for the user. The user may provide the userinterface 2100 with the user input by touching the user interface 2100,speaking to the user interface 2100, or any other suitable input. Theuser interface 2100 may request that a user enter other user input, suchas an exertion level, a difficulty of an exercise, or the like. Further,a virtual coach may read the question to the user by saying (via aspeaker) “What is your knee pain level today? Please say it out loud.”The processing device may be configured to execute the virtual coach toprovide coaching and instructions to a user on how to use the exercisemachines 100, an exercise, or any other suitable information. Theinstructions and/or coaching may be a prerecorded or dynamic virtualcoach (e.g., a trainer or a physical therapist) and provide commands,instructions, and/or tips via audio and/or video. For example, thevirtual coach may provide tips on posture and form while performing anexercise or using the exercise machine. The virtual coach may providemotivational content, such as words of encouragement to the user. Thevirtual coach may be provided randomly during the video game and/or itmay be based on input and/or data from the user and/or sensors. Thevirtual coach may be data-driven. The processing device can receive userinput data, sensor data, tracker data, historical data, and/or any othersuitable information to obtain information, such as the difficulty levelof the exercise, whether the user likes the persona of the virtualcoach, and provide audio and/or visual coaching to the user.

FIG. 22 illustrates an example user interface 2200 presenting a pedalsetting for a session as determined by a machine learning model. Thepedal setting may be dynamically determined by the machine learningmodel 60 for a particular exercise based on the exercise level of theuser, the range of motion of the user, the characteristics of the user,or some combination thereof. The user interface 2200 may also include aprompt for the user to watch a tutorial for an exercise prior to theuser actually beginning the exercise. It should be noted the virtualcoach may provide audio/video instructions pertaining to the pedalsetting for the session.

FIG. 23 illustrates an example user interface 2300 presenting anexercise session determined for a user by a machine learning model. Theexercise session includes 5 sections: warm up cycle, seated march,sit-to-stand, calf raise, and hamstring curl. The total time for theexercise session is 18 minutes. The exercise session may be generated bythe machine learning model 60 as described herein and may be presentedon the user interface 2300 of the computing device 12 associated withthe exercise machine 100. The virtual coach may be executed by thecomputing device 12 to provide audio/video instructions and descriptionpertaining to the exercise session.

FIG. 24 illustrates an example user interface 2400 presenting details ofa particular exercise for a user to perform. The user interface 2400 mayinclude details of a particular exercise: the seated knee march. Forexample, the user interface 2400 presents that 2 sets of 10 reps are tobe performed by the user and an optional 30 second of rest between sets.The virtual coach may provide audio/video instructions and descriptionpertaining to the details of the exercise.

FIG. 25 illustrates an example user interface 2500 presenting anincentive, reward, congratulatory message, etc. to the user forcompleting a set. For example, the user interface 2500 presents anotification that says “Nice work! You've finished 1 set of 2. ” Thenthe user interface 2500 presents a notification to begin the next setafter the 30 second rest period expires. It should be noted that thevirtual coach may provide audio/video pertaining to the incentive,reward, congratulatory message. Such interaction with the virtual coachmay inspire the user to continue the exercise session and complete thenext set, thereby advancing their exercise plan and recovery rate.

FIG. 26 illustrates an example user interface 2600 that includes optionsfor the user to indicate whether an exercise is too easy or too hard.During performance of an exercise, the user may select the options fortoo easy or too hard on the user interface 2600 or say the words “tooeasy” or “too hard”. Such selection may cause the machine learning model60 to alter the exercise, the exercise session, or both. For example,the cloud-based computing system 16 may increase or decrease theintensity of the exercise by modifying a parameter (e.g., resistance,speed, etc.) of the exercise machine 100. Further, if the options fortoo easy or too hard are chosen more than a threshold number of times,the machine learning model 60 may remove the exercise from the exercisesession, add another exercise to the exercise session, switch to adifferent exercise, or the like.

FIG. 27A illustrates an example method 2700 for generating, using amachine learning model, an exercise session for a user and causing avirtual coach to provide instructions pertaining to the exercisesession. The operations can be used to improve compliance with anexercise plan. The method 2700 may be performed by processing logic thatmay include hardware (circuitry, dedicated logic, etc.), firmware,software, or a combination of them. The method 2700 and/or each of theirindividual functions, subroutines, or operations may be performed by oneor more processing devices of a control system (e.g., cloud-basedcomputing system 16, computing device 12 of FIG. 1) implementing themethod 2700. The method 2700 may be implemented as computer instructionsthat are executable by a processing device of the control system (e.g.,a computer-readable medium may be used to store instructions that, whenexecuted, cause a processor perform the following steps or processes ofthe method 2700 ). In certain implementations, the method 2700 may beperformed by a single processing thread. Alternatively, the method 2700may be performed by two or more processing threads, each threadimplementing one or more individual functions, routines, subroutines, oroperations of the methods. Various operations of the method 2700 may beperformed by one or more of the cloud-based computing system 16, thecomputing device 12, and/or the computing device 15 of FIG. 1.

At 2702, the processing device may receive a set of inputs. The set ofinputs may include an indication of a level of pain of the user, a rangeof motion of a body part of the user, a set of characteristics of theuser, or some combination thereof. The indication of the level of painof the user may be entered by the user using any suitable peripheraldevice (e.g., microphone, keyboard, touchscreen, mouse, etc.) at aparticular user interface 18 displayed on a computing device 12. Therange of motion of the body part of the user may be determined by theprocessing device by the user performing a baseline exercise for acertain amount of time. The baseline exercise may include setting apedal at an initial position and having the user cycle at that positionfor a period of time. If the user does not experience any pain after theperiod of time, the pedal may be moved to a second position and the usermay cycle for the period of time again. If the user experiences pain,then the range of motion of the user may be determined based on theprevious position of the pedal when the user was able to cycle withoutpain. As may be appreciated, if the user does not experience pain, theposition of the pedal may continue to change until the user experiencespain and the ROM of the user may be determined based on the priorposition where the user did not experience pain. The characteristics ofthe user may include an age of the user, a height of the user, a weightof the user, a gender of the user, a condition that caused the pain inthe body part, one or more procedures perform on the user, a goal of theuser, whether the user is in a pre-procedure stage or a post-procedurestage, or some combination thereof. The characteristics may be includedin a user profile for the user that is stored at the cloud-basedcomputing system 16, the computing device 12, the computing device 15,or both.

At 2704, the processing device may determine, based on the set ofinputs, an exercise level of the user. The exercise levels may rangefrom 1-5, where 1 is the lowest exercise level and 5 is the mostadvanced exercise level. Any suitable range of exercise levels may beused. The following chart illustrates an example of how the exerciselevel may be determined:

If Pain Is & ROM Is Then Level Is 8-10 1-2 1 8-10 3-4 2 8-10 5 3 5-7 1-2 1 5-7  3-4 2 5-7  5 3 1-4  1-2 1 1-4  3-4 2 1-4  5 4 0 5 5

At 2706, the processing device may generate, using the machine learningmodel 60, an exercise session for the user by selecting, based on theexercise level of the user, one or more exercises to be performed by theuser using the exercise machine 100. In some embodiments, a datastructure may include entries for a set of exercises (e.g., tens,hundreds, thousands, etc.) that are each tagged with an exercise level.For example, the processing device may tag each exercise of the set ofexercises with a respective user exercise level. The machine learningmodel 60 may access the data structure to select the exercises for theexercise session by filtering the set of exercises, as further discussedwith reference to FIG. 28.

At 2708, the processing device may cause initiation of the exercisesession on the exercise machine 100 and a virtual coach executed by thecomputing device 12 associated with the exercise machine 100 to provideinstructions pertaining to the exercise session. The virtual coach maybe driven by artificial intelligence via one or more trained machinelearning models 60. For example, the trained machine learning models mayreceive various inputs, such as the exercise session for the user, theexercise being performed, instructions pertaining to the exercise beingperformed, completion of the exercise being performed, progress of theexercise being performed, and may be trained to provide certain outputsbased on the inputs. The virtual coach may output audible noise (e.g.,speech) that pertain to the various inputs. For example, the virtualcoach may say, via a speaker of the computing device 12, encouragingwords while a user is performing an exercise, congratulatory words whenthe user completes an exercise, instructions when the user is about tostart another exercise, and the like. The virtual coach may have apersona (e.g., a cheerleader type of persona, a drill sergeant type ofpersona) that is selected based on progress of the user, feedback of theuser, or both, as described further below with reference to FIG. 30.

In some embodiments, the processing device may receive, from the userwhile the user is performing an exercise of the one or more exercises inthe exercise session, feedback pertaining to the exercise. The feedbackmay include an indication that the exercise is too easy or too hard. Forexample, the user may use a display screen or microphone of thecomputing device 12 to enter or say the exercise is “too easy” or “toohard”. Responsive to receiving the feedback, the processing device maycause an intensity of the exercise to increase or decrease. For example,if the user says “too easy” the intensity of the exercise may beincreased. If the user says “too hard”, the intensity of the exercisemay be decreased. Other dimensions, parameters, characteristics, etc. ofthe exercise or exercise session may be changed based on whether thefeedback is too easy or too hard. For example, the other dimensions,parameters, characteristics, etc. may include a number of sets, a numberof repetitions, a hold time, a rest time, and the like. When one of thedimensions, parameters, characteristics, etc. changes, the virtual coachmay provide an indication of the change. For example, the virtual coachmay say, via a speaker of the computing device 12, “The intensity forthis exercise has increased”.

In some embodiments, the processing device may track how many times theuser has provided the feedback for a particular exercise in an exercisesession or across every exercise session in an exercise plan for theuser. Responsive to determining the feedback has been received more thana threshold number of times (e.g., 3, 4, 5, etc.), the processing devicemay control, in real-time or near real-time, the exercise machine 100 toinitiate a more advanced exercise than the exercise currently beingperformed, a less advanced exercise than the exercise currently beingperformed, or the like. Further, the processing device may remove theexercise for which the feedback was received more than the thresholdnumber of times from subsequent exercise sessions and replace it withanother exercise. The processing device may cause the virtual coach toprovide an indication via the computing device 12 (e.g., voice emittedthrough the speaker, graphic on the user interface 18, text on the userinterface 18, or the like) of the change to the exercise.

In some embodiments, the processing device may monitor the progress ofthe user while the user uses the exercise machine to perform the one ormore exercises. The progress may include an amount of time the userperforms the one or more exercises, the range of motion of the userwhile the user performs the one or more exercises, the level of pain ofthe user while the user performs the one or more exercises, whether theuser completes the one or more exercises, an indication of the user of alevel of difficulty of the one or more exercises, or some combination.The progress may be determined based on measurement data received fromany sensor associated with the exercise machine 100, any user feedbackreceived by the computing device 12, and the like. The user may use anysuitable peripheral to input the level of difficulty (e.g., too hard ortoo easy) while the user performs the exercises. The processing devicemay adjust, by executing the machine learning model 60, a subsequentexercise session based on the progress of the user. The adjusting may bebased on advancing the exercise level of the user to a next exerciselevel, achieving a desired goal as defined by the user, a medicalprofessional, or both, or some combination thereof.

In some embodiments, the processing device may monitor progress of theuser while the user uses the exercise machine 100 to perform the one ormore exercises. The processing device may cause, based on the progressof the user, an incentive, reward, or both to be elicited by thecomputing device 12 associated with the exercise machine 100. Theincentive, reward, or both may include an animation, video, audio,haptic feedback, image, push notification, email, text, or somecombination thereof. The processing device may cause the virtual coachto perform an encouraging action (e.g., shoot virtual fireworks on theuser interface 18, cause an avatar displayed on the user interface 18 todance or give a virtual high five, emit an audible noise from thespeaker congratulating the user). Providing incentives, rewards, or bothmay encourage the user to continue to perform exercises and comply withthe exercise session, which in turn, may decrease the amount of time ittakes for the user to achieve their goal. Reducing the amount of time ittakes for the user to achieve their goal may include technical benefitsbecause if the user achieves their goal faster, the computing device 12,exercise machine 100, and/or the cloud-based computing system 16 maysave computing resources (e.g., processing, memory, network) by nothaving to execute as long to guide the use through the exercise plan.That is, if the user does not comply with the exercise plan efficientlyor as directed, then the exercise plan may be adjusted to add additionalexercise sessions, thereby causing the computing device 12, exercisemachine 100, and/or the cloud-based computing system 16 execute longerand waste computing resources until the user achieves their goal.

In some embodiments, the processing device may determine when a numberof incentives, rewards, or both elicited by the computing device 12satisfy a threshold value (e.g., 3, 4, 5). Responsive to determiningthat threshold value is satisfied, the processing device may cause acertificate to be transmitted to the computing device 12 and associatedwith an account of the user using the exercise machine 100. For example,the certificate may be stored in a digital wallet of the user's accountin the application 17 executing on the computing device 12. In someembodiments, the certificate may have a particular value that may beexchanged for certain items (e.g., gift certificate, clothing, coupons,discounts, etc.).

In some embodiments, the processing device may determine, by executingthe machine learning model 60, a set of audio segments for the virtualcoach to say while the user performs the one or more exercises. Theaudio segments may be based on a state of the exercise (e.g., beginning,middle, end), progress of the user performing the exercise, or anysuitable information. For example, at the initiation of the exercise,the audio segment may provide instructions to the user on the details ofthe exercise (e.g., 2 reps, 30 seconds, etc.). Based on the progress ofthe user, the audio segment may say “pedal faster” if the user is notpedaling fast enough, “good job” if the user is satisfying the criteriafor the exercise, “almost finished” if the user is almost finished withthe exercise, or the like. The audio segments may be dynamicallydetermined, in real-time or near real-time, by the machine learningmodel 60 based on the inputs described above. It should be noted thatreal-time or near real-time may refer to a relatively short amount oftime (e.g., less than 5 seconds) after an action occurring.

In some embodiments, the processing device may determine, by executingthe machine learning model 60, a schedule of a set of exercise sessionsto be performed by the user to achieve a desired goal specified by theuser, a medical professional (e.g., physical therapist), or both. Themachine learning model may be trained to determine the schedule based onvarious inputs, such as the desired goal (e.g., full recovery, near fullrecovery at a fastest pace possible, strength improvement, flexibilityimprovement, etc.), a procedure performed on the user, characteristicsof the user (e.g., age, weight, height, etc.), a daily schedule of theuser (e.g., job schedule, parenting schedule, school schedule, etc.),and the like. The schedule may be optimized for the user and may complywith the various inputs described above.

In some embodiments, the virtual coach may be controlled, in real-timeor near real-time, by the machine learning model 60. For example, thevirtual coach may provide indications (e.g., emit audible noises,present various screens or notifications or indications or avatars orgraphics, etc.) via the computing device 12 as parameters of theexercise, exercise session, exercise machine 100, etc. change, or ascharacteristics or progress of the user changes.

FIG. 27B illustrates an example data structure 2750 including a set ofexercises tagged by exercise level of a user. As depicted, the datastructure 2750 (e.g., table, database, linked list, blockchain, etc.)includes columns for Exercise, Description, Image, Levels (taggedportion), Sections, Component (Exercise Goal), Swap it out with, TooEasy, Too Hard, Reps (starting level), # of sets (starting level), TimePer Rep, Rest Time Per Set, Body Part Exercised, and Intensity. In someembodiments, different columns may be used and not all of the onesdepicted may be used.

The exercise Sitting Knee Extension has been tagged as a suitableexercise for levels 1, 2, and 3, and is an option for the sectioncomprising Warm Up. It should be note that each exercise session mayinclude various sections: warm up, cardio, strength, cycle, cool down,flexibility, etc. Each section of an exercise session may be assignedone or more exercises that are appropriate for that section, based onthe entry in the data structure 2750, and the exercise level of the userthat matches the level in the data structure 2750.

FIG. 28 illustrates an example method 2800 for filtering a set ofexercises to obtain one or more exercises to include in an exercisesession for a user. The operations can be used to improve compliancewith an exercise plan. The method 2800 may be performed by processinglogic that may include hardware (circuitry, dedicated logic, etc.),firmware, software, or a combination of them. The method 2800 and/oreach of their individual functions, subroutines, or operations may beperformed by one or more processing devices of a control system (e.g.,cloud-based computing system 16, computing device 12 of FIG. 1)implementing the method 2800. The method 2800 may be implemented ascomputer instructions that are executable by a processing device of thecontrol system (e.g., a computer-readable medium may be used to storeinstructions that, when executed, cause a processor perform thefollowing steps or processes of the method 2800). In certainimplementations, the method 2800 may be performed by a single processingthread. Alternatively, the method 2800 may be performed by two or moreprocessing threads, each thread implementing one or more individualfunctions, routines, subroutines, or operations of the methods. Variousoperations of the method 2800 may be performed by one or more of thecloud-based computing system 16, the computing device 12, and/or thecomputing device 15 of FIG. 1.

At 2802, the processing device may filter a set of exercises to obtainthe one or more exercises for a particular exercise session in anexercise plan. Operation 2802 may include operations 2804, 2806, 2808,2810, and/or 2812.

At 2804, the processing device may identify, based on the tagging of theexercises in the data structure, a subset of exercises having therespective user exercise level that matches the exercise level of theuser. At 2806, the processing device may identify a first subset ofexercises having a respective section of a set of sections, wherein theset of sections include warm-up, cycling, strength, flexibility, or somecombination thereof. At 2808, the processing device may identify asecond subset of exercises that result in a desired outcome specified bya medical professional, wherein the desired outcome pertains toincreasing a range of motion, mobility, strength, flexibility, or somecombination thereof. At 2810, the processing device may identify, usinga historical performance of the user, a third subset of exercises thathave been performed by the user less than a threshold number of times.At 2812, the processing device may identify, based on feedback from theuser, a fourth subset of exercises that have been performed by the userand indicated as being too easy or too hard for the user.

In some embodiments, the processing device may select at least one ofthe subset of exercises, the first subset of exercises, the secondsubset of exercises, the third subset of exercises, or the fourth subsetof exercises as the one or more exercises for the exercise session. Thatis, any combination of the subset, the first subset, the second subset,the third subset, and the further subset of exercises may be selected asthe one or more exercises for the exercise session.

FIG. 29 illustrates an example method 2900 for adjusting an exercisesession based on user feedback. The operations can be used to improvecompliance with an exercise plan and may improve the user's experienceusing the computing device 12 and/or the exercise machine 100. Themethod 2900 may be performed by processing logic that may includehardware (circuitry, dedicated logic, etc.), firmware, software, or acombination of them. The method 2900 and/or each of their individualfunctions, subroutines, or operations may be performed by one or moreprocessing devices of a control system (e.g., cloud-based computingsystem 16, computing device 12 of FIG. 1) implementing the method 2900.The method 2900 may be implemented as computer instructions that areexecutable by a processing device of the control system (e.g., acomputer-readable medium may be used to store instructions that, whenexecuted, cause a processor perform the following steps or processes ofthe method 2900). In certain implementations, the method 2900 may beperformed by a single processing thread. Alternatively, the method 2900may be performed by two or more processing threads, each threadimplementing one or more individual functions, routines, subroutines, oroperations of the methods. Various operations of the method 2900 may beperformed by one or more of the cloud-based computing system 16, thecomputing device 12, and/or the computing device 15 of FIG. 1.

At 2902, the processing device may receive, from the user while the useris performing an exercise of the one or more exercises, feedbackpertaining to the exercise, wherein the feedback includes an indicationof a level of difficulty of the exercise. For example, the feedback maybe entered by the user using any suitable peripheral (e.g., microphone,touchscreen, mouse, keyboard, etc.) of the computing device 12. Thefeedback may include the user saying the exercise is too easy or toohard.

At 2904, the processing device may determine whether the feedback hasbeen received more than a threshold number of times for the exercise. At2906, responsive to determining the feedback has been received more thanthe threshold number of times for the exercise, the processing devicemay adjust, in real-time or near real-time, the exercise session. Insome embodiments, adjusting the exercise session may include changing toanother exercise, controlling the exercise machine to stop the exercise,removing the exercise from the exercise session, changing an intensityof the exercise, or some combination thereof. At 2908, the processingdevice may cause the virtual coach to provide an indication of theadjustment. The indication may be provided via the user interface 18, aspeaker of the computing device 12, or the like.

FIG. 30 illustrates an example method 3000 for selecting a persona forthe virtual coach. The operations can be used to improve compliance withan exercise plan and may improve the user's experience using thecomputing device 12 and/or the exercise machine 100. The method 3000 maybe performed by processing logic that may include hardware (circuitry,dedicated logic, etc.), firmware, software, or a combination of them.The method 3000 and/or each of their individual functions, subroutines,or operations may be performed by one or more processing devices of acontrol system (e.g., cloud-based computing system 16, computing device12 of FIG. 1) implementing the method 3000. The method 3000 may beimplemented as computer instructions that are executable by a processingdevice of the control system (e.g., a computer-readable medium may beused to store instructions that, when executed, cause a processorperform the following steps or processes of the method 3000 ). Incertain implementations, the method 3000 may be performed by a singleprocessing thread. Alternatively, the method 3000 may be performed bytwo or more processing threads, each thread implementing one or moreindividual functions, routines, subroutines, or operations of themethods. Various operations of the method 3000 may be performed by oneor more of the cloud-based computing system 16, the computing device 12,and/or the computing device 15 of FIG. 1.

At 3002, the processing device may select, for the virtual coach, apersona from a plurality of personas. The virtual coach may beimplemented in computer instructions stored in a memory device andexecutable by a processing device. The virtual coach may include aparticular voice (e.g., male, female) and have a particular persona. Thepersona may be randomly selected at first and the user's response to thepersona may be tracked over time. The response may include whether theuser performs the exercises completely or incompletely as the virtualcoach guides the user through the exercises. The personas may range froma cheerleader type that provides a lot of encouragement to a drillsergeant type that is more aggressive, harsher, stricter, and/or demandscompliance with the exercise or demands the user tries harder.

At 3004, the processing device may cause the virtual coach to provideinstructions as the user performs the one or more exercises. Theinstructions may be provided visually on the user interface 18, audiblyvia a speaker of the computing device 12, or both.

At 3006, the processing device may monitor a parameter associated withthe user while the user performs the one or more exercises. Theparameter may include a vital sign (e.g., heartrate, blood pressure),sensor measurement data (e.g., ROM, pressure exerted on pedals, etc.),characteristics of the user (e.g., respiratory rate, temperature,perspiration, etc.). The monitoring may be based on any suitable sensormeasurement data associated with the user, the exercise machine 100, orboth. In some embodiments, the parameter pertains to a progress of theuser, an indication of whether the user likes the persona of the virtualcoach, or both. For example, the user may provide feedback that theylike the persona of the virtual coach via the user interface 18 or byspeaking to the computing device 12 via a microphone.

At 3008, the processing device may select, based on the parameter, asubsequent persona for the virtual coach. For example, if the userindicated the user does not like the persona, the processing device mayselect a different persona for a subsequent exercise and/or exercisesession.

At 3010, the processing device may switch, in real-time or nearreal-time, based on the parameter, to a different persona for thevirtual coach while the user performs the one or more exercises.Dynamically switching may be based on whether the user is performing theexercise well or not. For example, if the user is pedaling atsubstantially slower rate than desired for the exercise, the processingdevice may determine the user is not responding well to the persona andmay switch to a different persona immediately during the exercise. Theprogress of the user may be tracked to see if the switch of personasimpacts the progress of the user. Further, if the user indicates theuser does not like the persona, the processing may switch to a differentpersona immediately while the user performs the exercise.

FIG. 31 illustrates an example user interface 1918 presenting anindication 3100 that an exercise is complete, resulting in the user'sbeing congratulated. For example, the indication 3100 states: “Good job!You exceeded your target load threshold(s). This exercise is complete.”The user interface 1918 may present visual representations 3102 and/or3104 for the left and right load measurements, respectively. In someembodiments, the visual representations 3102 and/or 3104 may benumerical values representing other the respective measurements. In someembodiments, the visual representation 3102 and/or 3104 may be bars on abar chart, lines on a line chart, or any suitable visual representation.

Further, the user interface 1918 may present one or more visualrepresentations 3106 of target load thresholds tailored for the user.For example, the one or more target thresholds may include a left targetthreshold, a right target threshold, or some combination thereof.Presenting the visual representations 3106 of the target thresholdsconcurrently with the real-time display of the measurements in thevisual representations 3102 and/or 3104 may enable the user to determinehow close they are to exceeding the target thresholds and/or when theyexceed the target thresholds.

FIG. 32 illustrates an example computer system 3200, which can performany one or more of the methods described herein. In one example,computer system 3200 may correspond to the computing device 12 (e.g.,control system), the computing device 15, one or more servers 28 of thecloud-based computing system 16 of FIG. 1. The computer system 3200 maybe capable of executing the application 17 and presenting the userinterface 18 and/or the user interface 22 of FIG. 1. The computer system3200 may be connected (e.g., networked) to other computer systems in aLAN, an intranet, an extranet, or the Internet. The computer system 3200may operate in the capacity of a server in a client-server networkenvironment. The computer system 3200 may be a personal computer (PC), atablet computer, a motor controller, a goniometer, a wearable (e.g.,wristband), a set-top box (STB), a personal Digital Assistant (PDA), amobile phone, a camera, a video camera, or any device capable ofexecuting a set of instructions (sequential or otherwise) that specifyactions to be taken by that device. Further, while only a singlecomputer system is illustrated, the term “computer” shall also be takento include any collection of computers that individually or jointlyexecute a set (or multiple sets) of instructions to perform any one ormore of the methods discussed herein.

The computer system 3200 includes a processing device 3202, a mainmemory 3204 (e.g., read-only memory (ROM), flash memory, dynamic randomaccess memory (DRAM) such as synchronous DRAM (SDRAM)), a static memory3206 (e.g., flash memory, static random access memory (SRAM)), and adata storage device 3208, which communicate with each other via a bus3210.

Processing device 3202 represents one or more general-purpose processingdevices such as a microprocessor, central processing unit, or the like.More particularly, the processing device 3202 may be a complexinstruction set computing (CISC) microprocessor, reduced instruction setcomputing (RISC) microprocessor, very long instruction word (VLIW)microprocessor, or a processor implementing other instruction sets orprocessors implementing a combination of instruction sets. Theprocessing device 3202 may also be one or more special-purposeprocessing devices such as an application specific integrated circuit(ASIC), a field programmable gate array (FPGA), a digital signalprocessor (DSP), network processor, or the like. The processing device3202 is configured to execute instructions for performing any of theoperations and steps discussed herein.

The computer system 3200 may further include a network interface device3212. The computer system 3200 also may include a video display 3214(e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)), oneor more input devices 3216 (e.g., a keyboard and/or a mouse), and one ormore speakers 3218 (e.g., a speaker). In one illustrative example, thevideo display 3214 and the input device(s) 3216 may be combined into asingle component or device (e.g., an LCD touch screen).

The data storage device 3208 may include a computer-readable storagemedium 3220 on which the instructions 3222 (e.g., implementing theapplication 17 or 21 executed by any device and/or component depicted inthe FIGURES and described herein) embodying any one or more of themethodologies or functions described herein are stored. The instructions3222 may also reside, completely or at least partially, within the mainmemory 3204 and/or within the processing device 3202 during executionthereof by the computer system 3200. As such, the main memory 3204 andthe processing device 3202 also constitute computer-readable media. Theinstructions 3222 may further be transmitted or received over a networkvia the network interface device 3212.

While the computer-readable storage medium 3220 is shown in theillustrative examples to be a single medium, the term “computer-readablestorage medium” should be taken to include a single medium or multiplemedia (e.g., a centralized or distributed database, and/or associatedcaches and servers) that store the one or more sets of instructions. Theterm “computer-readable storage medium” shall also be taken to includeany medium that is capable of storing, encoding or carrying a set ofinstructions for execution by the machine and that cause the machine toperform any one or more of the methodologies of the present disclosure.The term “computer-readable storage medium” shall accordingly be takento include, but not be limited to, solid-state memories, optical media,and magnetic media.

FIG. 33 illustrates an example user interface 3300 presenting a userprofile of the application 17 on the computing device 12. The userinterface 3300 may include various information for a user to complete inthe user profile. For example, the information may include a name (“JohnDoe”), an age (“45”), a weight (“200”), an email (johndoe@email.com), aheight (“6 ”), a gender (“Male”), one or more medical conditions(“Obesity”, “Depression”), one or more medical procedures (“KneeReplacement”), and one or more statuses (“Rehabilitation”). Using aninput peripheral, such as a keyboard, mouse, touchscreen, microphone,etc., the user may enter the information on the user interface 3300. Insome embodiments, the cloud-based computing system 16 may providecomputer instructions that cause presentation of the user interface3300. The user interface 3300 may be a website executed by a webbrowser, or a standalone application installed on the computing device12 and in communication with the cloud-based computing system 16. Thecomputing device 12 may be communicatively coupled to the exercisemachine 100, and in some embodiments, the user may, using the computingdevice 12, control operating parameters of the exercise machine 100.

In some embodiments, the cloud-based computing system 16 and/or thecomputing device 12 may connect to and/or use an application programminginterface (API) exposed by a third-party entity, such as an electronicmedical records (EMR) system, and/or a social network system. The APImay be used by the application 17 to extract information pertaining toEMR of the user, if proper authorization is given and authorization of auser account is completed. The EMR information may automaticallypopulate the appropriate fields in the user profile. For example, themedical procedures identified in the EMR information may be populated.In some embodiments, the format of the data obtained by the API may bein a different format than the format the application 17 uses. In suchan instance, the application 17 may transform the data's format into anacceptable format (e.g., extensible markup language (XML)) for theapplication 17. In some embodiments, the application 17 may use the APIto access the user's information on a social media or social networksystem (e.g., Facebook®, Twitter®, Instagram®, etc.) to obtaininformation publicly available on the social network system.

FIG. 34 illustrates an example user interface 3400 for selecting aphysical activity goal and a pain level. As depicted, graphical element3402 may be used to select the physical activity goal. The graphicalelement 3402 is itself depicted as a dropdown menu but any suitablegraphical element may be used (e.g., input box, checklist, etc.). In theexample, the user has used a touchscreen to select a physical activitygoal “Play with grandchildren”. Further, graphical element 3404 may beused to enter a pain level the user is currently experiencing. Graphicalelement 3404 may be any suitable graphical element capable of receivinginput.

The received input of the physical activity level and the pain level maybe used by the one or more machine learning models 60 to generate animproved exercise plan. For example, the machine learning model maydetermine, using a data source including various associations,including, for example, the levels of attainment associated withachieving the physical activity level, where the levels of attainmentmay include range of motion, strength, endurance, balance, intelligence,neurological responsiveness, emotional well-being, and mobility.Further, the machine learning model 60 may determine which body portionsto target for the various levels of attainment, and which exercises toselect to include in the exercise plan that target the appropriate bodyportions. In some embodiments, the pain level reported by the user maybe used to select exercises, difficulty levels of the exercises, and thelike.

In some embodiments, upon the user's selecting the physical activitygoal and the pain level, an onboarding protocol that uses a baselinefitness test may be initiated. For example, FIG. 35 illustrates anexample user interface 3500 presenting information pertaining to a firstexercise 3502 of a baseline fitness test. The baseline fitness test mayselect an exercise for the user to perform, and the exercise difficultylevel for the first exercise 3502 may be set to an easiest difficultylevel. As the user performs the first exercise, the following may beobtained: characteristics of the user, performance measurements of theuser, user-reported difficulty level of the exercise, user-reported painlevel of the user, and the like. For example, the user may select agraphical element 3504 (button) to indicate that the first exercise istoo easy.

Selection of the graphical element 3504 may cause the machine learningmodel 60 to select a next exercise that is more difficult. Theonboarding protocol may include exercises having tiered difficultylevels and may select for subsequent exercises for the user to perform,wherein the subsequent exercises advance in difficulty until the userhas either completed all of the exercises or reached a point where theuser can no longer perform the exercise because it is too difficult orpainful. The machine learning model 60 may determine a fitness level forthe user based on a completion state (e.g., a degree of completion, apercentage of completion, a value of completion, etc.) of a lastexercise performed by the user. The machine learning model 60 may selecta difficulty level for each exercise in the improved exercise plan byassociating the difficulty level for each exercise with the fitnesslevel of the user.

In some embodiments, a multimedia segment (e.g., recording or feed) maybe presented in a digital media player 3506. The multimedia segment mayinclude video and/or audio of a coaching character providinginstructions and guidance on how to perform the first exercise. Variousoptions may be provided by the digital media player that enable the userto play, pause, or stop the multimedia segment. There may be options toenable the user to fast forward or rewind the multimedia segment, aswell.

FIG. 36 illustrates an example user interface 3600 presentinginformation pertaining to a second exercise 3602 of the baseline fitnesstest. As depicted, a different multimedia segment is being played in thedigital media player 3506. The new multimedia segment may include videoand/or audio of a coaching character providing information and guidanceto the user pertaining how to perform the second exercise 3602. Thesecond exercise 3602 may be selected as a result of the user indicatingthe previous exercise was too hard or too easy. In some embodiments, thesecond exercise 3602 may be selected by default by the machine learningmodel 60. After the baseline fitness test is performed by the user, themachine learning model 60 may determine the fitness level of the user,and use the fitness level of the user to select the exercises and/ortheir difficulty levels in the improved exercise plan.

FIG. 37 illustrates an example user interface 3700 presenting agenerated exercise plan 3702 for a user. The user interface 3700presents a first week of exercises for the user to perform. The exerciseplan 3702 indicates “People with similar characteristics (user fitnesslevel) as you are able to play with their grandchildren within 6 weeksby following this exercise plan.” As described further herein, theexercises included in the exercise week 1 schedule may include exercisesprioritized for the levels of attainment associated with the selectedphysical activity goal. For example, the exercises depicted all relateto cardiovascular health and moving, which correspond with a mobilityand endurance level of attainment, both of which may be ranked higherfor a physical activity goal of playing with grandchildren. Thistechnique may enable ensuring the appropriate body portions of the userare targeted via proper exercises to achieve the levels of attainment,and thereby achieve the physical activity goal.

As further depicted, each exercise includes an energy consumption metric(“50”). The energy consumption metric may vary for each exercise and itmay provide a target metric for the user to achieve during eachexercise. The energy consumption metric may be based on a combination ofvarious types of information and metrics associated therewith, such as ametabolic indicator associated with performing the exercise, fitnessresults of the user, and/or a user-reported pain level of the user,among other information. The energy consumption metric may be determinedfor the user while they perform the exercise, and when the target energyconsumption metric has been exceeded, the user may be done with theexercise. The application 17 may track the user's progress over time ifand when the user exceeds or meets the target energy consumption metric.Each determined energy consumption metric for each exercise may besummed to determine an energy score associated with an amount of energyit will take to achieve the physical activity goal. If the summed energyconsumption metrics equal or exceed the energy score, then the user mayhave enough energy to achieve the physical activity goal. As may beappreciated, the user may exceed or match the energy score faster orslower than predicted based on a number of factors, such as theirperformance, their drive, their health (e.g., physical and mental),their compliance with the exercise plan, and the like.

FIG. 38 illustrates an example user interface 3800 presentinginformation pertaining to a user's comorbidities 3802, week one of anexercise plan 3702, and evidential source 3804, wherein the evidentialsource 3804 pertains to the comorbidities and the exercise plan. Theinformation may be stored and accessed in the data source 67. Themachine learning model 60 may be trained to receive input data (e.g.,characteristics of the user, performance measurements of the user,user-reported pain levels, user-reported difficulty levels of exercises,etc.) and to output comorbidity information. For example, the machinelearning model 60 may match the input data of the user with one or moreother users or cohorts to determine that the one or more similarlysituated users or cohorts are associated with certain comorbidities, andto output the certain comorbidities 3802 on the user interface 3800. Theresources used by the data source 67 and the machine learning model 60may be curated by health professionals and may be associated withcertifications of authenticity, board review, board approval,evidence-based guidelines or the like. As depicted, evidential sources3804 are presented in a pop-up window overlaid on the exercise plan 3702when the user hovers over or “taps” on a particular part of the userinterface 3800 with an input peripheral (e.g., touchscreen). Theevidential sources 3804 provide information about the resources used bythe machine learning models 60 to determine the comorbidity information3802 and the exercise plan 3702. For example, the evidential source 3804indicates one resource used was “Rehab for Knee Replacements”—“Dr. AliceSmith”, and another resource used was “‘Defining comorbidityimplications understanding health and health service’. Ann Fam Med.2009; 7(4): 357-363”. The comorbidity information 3802 indicates “Peoplewith similar characteristics (obesity) as you are at risk for thefollowing comorbidities: Type 2 diabetes; High blood pressure.”

FIG. 39 illustrates an example user interface 3900 presentinginformation pertaining to week two of an exercise plan 3702 for a user.As depicted, the exercises included in week two of the exercise plan3702 target the upper body of the user, whereas the exercises in weekone of the exercise plan 3702 targeted the lower body of the user andcardiovascular health of the user. To ameliorate boredom, improveengagement, improve compliance, and the like, the exercises may beselected and arranged differently between the various weeks of theexercise plan 3702. Further, to focus on different levels of attainmentand their associated body portions that are used to achieve the levelsof attainment, the exercises may be differentiated between weeks. Theexercises selected in week two in the exercise plan 3702 may be relatedto the upper body to improve strength to enable a user to pick up theirgrandchild, for example, while playing with the grandchild. The exerciseplan 3702 also includes other information pertaining to each exercise,such as a type of exercise, a number of repetitions, a number of sets, afrequency, an amount of weight, and/or the energy consumption metric.

FIG. 40 illustrates an example user interface 4000 presentinginformation pertaining to a target energy consumption metric 4002 for anexercise. In some embodiments, the user may initiate the exercise plan3702 by selecting from any of the user interfaces depicted herein thefirst exercise to begin. As depicted, the multimedia segment 4004 beginsplayback via the digital media player 4006. The target energyconsumption metric 4002 for the first exercise in week one of theexercise plan 3702 is depicted as being 50. In some embodiments, theuser's energy consumption metric may be dynamically determined as theuser performs the exercise and also presented in the user interface 4000such that the user is apprised of their progress toward meeting orexceeding the target energy consumption metric. Also, as depicted, anotification 4008 is presented in the user interface 4000. Thenotification 4008 may be selected and presented by one or more machinelearning models 60 trained to identify when a user may benefit from acertain notification. For example, the machine learning model 60 maydetermine that the user has reported the exercise is too hard, and maydetermine to provide a motivational quote, such as “Keep it up John Doe,you got this!”

FIG. 41A-41E illustrates an example data source 67 including informationpertaining to exercises and physical activity goals. As depicted in FIG.41A, a table 4100 includes information pertaining to exercises andphysical ability goals (also referred to as a “life goal” in the table4100 ). The table 4100 has columns labeled “ID”, “Name”, “MET Score”,“Description”, “Difficulty (E, M, H)”, “Exercise Levels”, “Sections”,“Life Goals”, and “Difficulty Decrease ID”. For example, a first rowhaving ID “1” indicates the exercise is named “Sitting Knee Extension”,has a MET score of “3.5”, has a description of “Start with feet onground while sitting in chair. Then lift one foot and straighten theknee and hold”, has a difficulty of “M” (medium), has exercise levels of“1,2,3”, is associated with sections “3,4”, and decreases difficulty IDof “2”. A MET score may also be referred to as a MET indicator and mayrefer to a ratio of working metabolic rate of a person relative to theperson's resting metabolic rate. Metabolic rate is the rate of energyexpended per unit of time. It may refer to one way to describe theintensity of an exercise or activity. The Sections may includeidentifiers for exercise sections, such as “1—warmup”, “2—cardio”,“3—strength”, “4—flexibility”, “5—cooldown”, and/or “6—cycling”.

FIG. 41B illustrates an example data source 67 including informationpertaining to levels of attainment associated with physical abilitygoals, as depicted in FIG. 41D. The levels of attainment may be alsoreferred to “components” in FIG. 1D. The levels of attainment table 4110may include identifiers (ID) and names (“1—Range of Motion (ROM)”,“2—Strength”, “3—Endurance”, “4—Balance”, “5—Mobility”, etc.). Each ofthe levels of attainment may be associated with one or more bodyportions. FIG. 41C depicts a table 4120 including identifiers (ID) andnames for body portions (“1—Hip Flexor”, “2—Posterior Hip”,“3—Hamstring”, “4—Lateral Hip”, etc.

FIG. 41D illustrates an example data source 67 including informationpertaining to the various physical ability goals, the body portions totarget to achieve the physical ability goal, and rankings of the levelsof attainment to prioritize when selecting exercises and scheduling anexercise plan. For example, row 2 having ID 2 is a physical ability goalnamed “Getting up/down stairs”. The body parts that need to be improvedare listed in a comma delimited list. That is, an ID or tag or key isincluded in the list and each of the IDs corresponds to a particularbody part, as shown in FIG. 41C. Further, the table 4130 includes aprioritization of which levels of attainment should be more heavilyweighted when selecting exercises. The component 1 (“ROM”) is shown asreceiving a priority of “1”, which in this example represents a highestpriority, whereas component 3 (“Endurance”) is shown as receiving apriority of “4”, which in this example represents a second to lowestpriority. Based on the rankings, the machine learning model 60 may betrained to select various types of exercises for the exercise plan.

FIG. 41C illustrates an example data source 67 including informationrelated to the relative weights each level of attainment and theirassociated percentages of exercises should be given when determining theexercise plan for a 6-week period of time. The relative weights for thelevels of attainment and percentages of exercises related to thoselevels of attainment that should be included in the exercise plan add upto a value of 1 for each week. For example, for the first week in thetable 4140, the first component receives a priority percentage of 0.4for its associated exercises, the second component receives a prioritypercentage of 0.4 for its associated exercises, the third componentreceives a priority percentage of 0.2 for its associated exercises, thefourth component receives a priority percentage of 0 for its associatedexercises, and the fifth component receives a priority percentage of 0for its associated exercises. The priority percentages add up to 1, asshown in the “Total %”. As depicted, in some embodiments, the prioritypercentages shift over the course of the 6-week time period, such thatmore exercises are selected for the fourth and fifth components as thetime progresses. Such a technique may enable ensuring that eachcomponent is properly worked on in order to achieve the physical abilitygoal. Also, a useful technique to ameliorate boredom is to vary thetypes of exercises performed each week, which may thereby lead toimproved compliance with the exercise plan.

FIG. 42 illustrates an example method 4200 for generating, based on aselected physical activity goal, an exercise plan. The method 4200 maybe performed by processing logic that may include hardware (circuitry,dedicated logic, etc.), firmware, software, or a combination of them.The method 4200 and/or each of their individual functions, subroutines,or operations may be performed by one or more processing devices of acontrol system (e.g., cloud-based computing system 16, computing device12 of FIG. 1) implementing the method 4200. The method 4200 may beimplemented as computer instructions executable by a processing device(e.g., a computer-readable medium may be used to store instructionsthat, when executed, cause a processor to perform the following steps orprocesses of the method 4200). In certain implementations, the method4200 may be performed by a single processing thread. Alternatively, themethod 4200 may be performed by two or more processing threads, eachthread implementing one or more individual functions, routines,subroutines, or operations of the methods. Various operations of themethod 4200 may be performed by one or more of the cloud-based computingsystem 16, the computing device 12, and/or the computing device 15 ofFIG. 1.

The method 4200 may enable generating an improved exercise plan for auser to perform using at least an exercise machine 100. At 4202, theprocessing device may receive data pertaining to the user. The datapertaining to the user may include at least one selection of a physicalactivity goal the user desires to achieve. In some embodiments, the usermay select more than one physical activity goal to achieve. The physicalactivity goals may include any activity that includes physical motion ofa portion of the user's body. For example, the physical activity goalsmay include ameliorating knee pain, traversing stairs, gardening,performing yardwork, playing, walking, running, meditating, learningfaster, improving concentration, improving focus, increasing responsetime to stimuli, improving relationships, improving sex drive, changinga state of mind, improving cardiovascular performance, improving heartrate, improving blood pressure, sitting without pain, standing withoutpain, feeling energized, performing more advanced exercises, performingmore exercises, carrying groceries, performing house chores, losingweight, or some combination thereof. In some embodiments, the user mayuse a user interface including one or more graphical elements to selectthe physical activity goal via the computing device 12.

In some embodiments, the processing device may execute a machinelearning model 60 trained to generate the exercise plan using an energyscore. An energy score may refer to an amount of energy it takes toachieve the physical activity goal. The energy score may be based on ametabolic indicator associated with performing each exercise. The energyscore may be an accumulation of at least all the metabolic indicatorsfor the exercises included in the exercise plan. An energy consumptionmetric may be associated with each exercise, and the energy consumptionmetric may be determined using the metabolic indicator for an exercise,user fitness test results, user-reported pain levels, an indication of apain level the user is in, heartrate, step count, blood pressure,perspiration, blood oxygen levels, body temperature, or some combinationthereof. The energy score may indicate that by performing the exercisesincluded in the exercise plan, the user will have enough energy to beable to achieve the physical activity goal. To determine whether theuser exerted enough energy for that particular exercise, progress towardthe energy score may be tracked at each exercise by calculating theenergy consumption metric. One or more graphical elements (e.g., charts,tables, etc.) may be used to dynamically visualize, by depictingrespective energy consumption metrics over a time series, the progressthe user is making toward the energy score.

Each of the physical activity goals may include one or more levels ofattainment to achieve. As described herein, the one or more levels ofattainment may refer to range of motion, strength, endurance, balance,intelligence, neurological responsiveness, emotional well-being,cardiovascular well-being, and/or mobility. The levels of attainment maybe associated with each physical activity goal. For example, a gardeningphysical activity goal may cause an exercise plan to be generated thatincludes exercises that improve the levels of attainment involving rangeof motion (e.g., kneeling down and bending over to garden) and endurance(e.g., energy consumed by gardening), more than the level of attainmentof mobility (e.g., since the user is typically not moving around verymuch while planting flowers or the like). The levels of attainment maybe quantified by, measured by, or associated with measurements (e.g.,range of motion extension and/or flexion angles, exerted forcemeasurements, amount of weight lifted, pressed, or curled, etc.),achievements (e.g., number of sets completed, number of repetitionscompleted, number of exercise sessions completed, weight lost, caloriesconsumed, steps walked, etc.), and the like. The measurements may beobtained via one or more sensors associate with the exercise machine100, the user, or the environment in which the user uses the exercisemachine 100. In some embodiments, the sensor may be a wearable devicethat the user wears while using and while not using the exercise machine100. For example, a step counting wearable may be worn by the user whilenot engaged in an active exercise session (e.g., while walking around agrocery store, etc.).

The data obtained from the sensors may enable monitoring the user'scomprehensive lifestyle to enable predicting when the user will achievethe physical activity goal accurately and to provide recommendationspertaining to the user's health. In some embodiments, data from eachuser of the application 17, the exercise machine 100, or both, may bemonitored and stored to enable training the machine learning models 60to perform one or more functions. For example, the machine learningmodels 60 may be generated using training data that enables the machinelearning models 60 to receive input data (e.g., characteristic of theuser, performance measurement, pain level of the user, etc.) pertainingto the users and/or the selected physical activity goal and to predict alength of time it will take the user to achieve the physical activitygoal if they comply with a particular exercise plan. The machinelearning models 60 may identify patterns between the data pertaining tothe user and other data pertaining to other users, and may determinethat the other users, by following the recommended exercise plan,achieved the same physical activity goals in the length of time. In someembodiments, the processing device may transmit the amount of time itwill take the user to achieve the physical activity goal to thecomputing device 12 for presentation. In some embodiments, the machinelearning models 60 may be generated and trained to receive the datapertaining to the user and determine one or more comorbidities of theuser (e.g., the user is at risk for diabetes because they are overweightand depressed, etc.). The machine learning models 60 may be trained toidentify patterns between the user and other users that have similardata and may determine the similarly situated users have thecomorbidities.

At 4204, the processing device may generate, by executing the artificialintelligence engine 65, the improved exercise plan. The artificialintelligence engine 65 may generate the machine learning models 60trained to perform the generating of the improved exercise plan usingthe data source. The improved exercise plan may include at least one setof exercises to be performed by the user to achieve at least one of theone or more levels of attainment associated with the physical activitygoal. The artificial intelligence engine 65 may use at least one datasource 67 configured to include information pertaining to one or moreinformation pertaining to one or more exercises and at least one of theone or more levels of attainment associated with the physical activitygoal.

In some embodiments, the data source may include a set of rankings andeach ranking of the set of rankings may pertain to a priority level foreach of the one or more levels of attainment pertaining to achieving thephysical activity goal. The set of exercises selected may be arranged inthe improved exercise plan based on the set of rankings. For example,for a physical activity goal of gardening, the levels of attainment ofrange of motion and endurance may be ranked higher than the level ofattainment of mobility, and as a result, exercises that target theportion(s) of the body associated with achieving range of motion andendurance may be prioritized in the exercise plan. Prioritizing thoseexercises may refer to including more exercises that are associated withthe higher ranking levels of attainment, including more repetitionsand/or sets for those exercises, including longer durations forperforming the exercises, and the like. In some embodiments, (i) a firstportion of the set of exercises associated with a level of attainmenthaving a certain ranking may be included in a set of initial exercisesto perform in the improved exercise plan, and (ii) a second portion ofthe set of exercises associated with the level of attainment havinganother ranking may be included as a set of last exercises to perform inthe improved exercise plan.

The data source 67 may include a first association between the physicalactivity goal and the one or more levels of attainment pertaining toachieving the physical activity goal, a second association between theone or more levels of attainment and one or more body portions of ahuman being, and a third association between the one or more bodyportions and one or more exercises that target the one or more bodyportions. The processing device may select the at least one set ofexercises based on the first association, the second association, andthe third association in order to provide an exercise plan that targetsthe body portions associated with the levels of attainment for thephysical activity goal. In some embodiments, the data source 67 mayinclude exercises that are curated by one or more health professionals,such as a trainer, a medical doctor, a physical therapist, a surgeon, orthe like. Further, the associations between the levels of attainment,exercises, body portions, and the like may be curated, filtered,reviewed, revised, and the like by the health professionals.

In some embodiments, to generate the improved exercise plan, theprocessing device may execute the one or more machine learning models 60trained to use an onboarding protocol, a fitness level of the user. Theonboarding protocol may include exercises with tiered difficulty levels.The onboarding protocol may advance a difficulty level for a subsequentexercise in the exercises when the user completes an exercise in theexercises. The fitness level of the user may be determined based on acompletion state (e.g., percentage, amount completed, performancemeasurement, user-report difficulty level, user-reported pain level,etc.) of a last exercise performed by the user. The machine learningmodels 60 may select a difficulty level for each exercise in theimproved exercise plan by associating the difficulty level for eachexercise with the fitness level of the user. This onboarding protocolmay be referred to as a baseline fitness test. One purpose of theonboarding protocol may be to match user's having particular fitnesslevels with exercises that have difficulty levels the user should beable to perform, and to optimize compliance and enhance an amount oftime it takes to achieve the physical activity goal.

At 4206, the processing device may transmit the improved exercise planto a computing device. For example, the improved exercise plan may betransmitted to computing device 12 and may be presented by the userinterface 18 of the application 17. In some embodiments, the processingdevice may execute the artificial intelligence engine 65 and/or machinelearning models 60 to transmit a signal to the exercise machine 100. Inresponse to the exercise machine 10 receiving the signal, a portion ofthe exercise machine 100 may be adjusted. The adjustment may be based onan attribute of an operating parameter specified in the improvedexercise plan. For example, an attribute of a speed operating parametermay indicate a particular pedaling exercise should be performed at 5miles per hour. When the exercise machine 100 receives the signalincluding a control instruction specifying the speed at which a motor ofthe exercise machine 100 should operate, a processing device of theexercise machine 100 may use the attribute of the operating parameter tocontrol the motor to operate at 5 miles per hour. There may be numerousattributes and numerous operating parameters specified in the improvedexercise plan. For example, each exercise selected may be associatedwith various attributes for various operating parameters. The exercisesand their attributes of operating parameters may be selected in order toimprove a rate at which the user achieves the physical activity goal,improve compliance, ameliorate boredom, enhance enjoyment, and the like.Based on characteristics of the user, performance measurements of theuser, user-reported difficulty levels of exercises, and/or user-reportedpain levels, the exercises and attributes of operating parameters maychange dynamically as a user performs the exercise plan.

In some embodiments, the processing device prompts, via the computingdevice 12, the user for feedback pertaining to one or more levels ofenjoyment while performing the improved exercise plan. The set ofexercises in the improved exercise plan are arranged in a performanceorder to ameliorate boredom based on the one or more levels ofenjoyment. For example, a machine learning model 60 may be trained tomatch patterns between the user and other users that performed exerciseplans, and to determine the other similar matched users indicated theyenjoyed a particular order of exercises in an exercise plan or did notenjoy the particular order. The machine learning model 60 may be trainedto select the exercises and/or an order of the exercises for a userbased on whether the user has experienced they enjoy the exercise and/ororder or other users indicated they enjoyed the exercise and/or order.In some embodiments, a healthcare professional (e.g., physicaltherapist, etc.) may use empirical evidence and/or data to select theexercises and/or performance order to maximize enjoyment, minimizeboredom, and/or maximize compliance. In some embodiments, the machinelearning models 60 may be trained to solve optimization (e.g.,maximization, minimization, etc.) problems to generate an improvedexercise plan.

In some embodiments, the processing device may determine a fitness levelof a user. The fitness level of the user may be determined by one ormore machine learning models 60 using data pertaining to the user. Thedata may include characteristics of the user, such as height, weight,age, medical history, etc., performance measurements (e.g., range ofmotion, speed, force, exercise duration, etc.), and indications of painlevels provided by the user. The fitness level of the user may include avalue or quantification using any suitable scale. For example, a scaleof 1 to 5 may be used to rate the fitness level of the user. A 1 mayindicate the user is a beginner or has a lowest fitness level and a 5may indicate the user is an elite athlete and has a highest fitnesslevel.

In some embodiments, the processing device may cause presentation of auser interface on the computing device 12, and the user interface maypresent multimedia of a coaching character configured to provideinstructions on how to perform an exercise of the improved exerciseplan. Based on the determined fitness level of the user, the processingdevice may modify the multimedia (e.g., video, and/or audio) of acoaching character (e.g., a human, a virtual representation of a human,an animated character, an augmented reality character, a virtual realitycharacter, etc.) performing an exercise in the improved exercise plan.The modifying of the multimedia may include slowing down playback of thevideo if the user has a low fitness level (e.g., 1, 2, etc.) or speedingup playback of the video if the user has a high fitness level (e.g., 3,4, 5, etc.). The multimedia may be selected from the data source 67. Thedata source 67 may store numerous multimedia files and each multimediafile may correspond with a particular exercise (e.g., a video of acoaching character performing a seated bar curl). The processing devicemay modify the multimedia playback according to the user's fitness levelsuch that the user has an engaging, productive, enjoyable, and/orappropriate exercise session.

In some embodiments, based on one or more factors, the processing devicemay pair an audio clip and a video clip to generate a paired audio andvideo clip. The one or more factors may include a fitness level of theuser, types of exercises included in the improved exercise plan, one ormore characteristics of the user (e.g., height, weight, age, gender,medical history, medical procedures, etc.), one or more performancemeasurements (e.g., range of motion, force, speed, distance, etc.), asensor measurement, feedback from the user pertaining to a difficulty ofan exercise, or some combination thereof. While the user performs theimproved exercise plan, the processing device may cause playback of thepaired audio and video clip. For example, if the user indicates that anexercise is too hard using the user interface, then the processingdevice may generate a paired audio and video clip that provides anencouraging statement (e.g., “Almost done!”, “You got this”, etc.)and/or slows down playback speed of the coaching character performingthe exercise in the video. Such a technical solution may also provideengaging, productive, enjoyable, and/or appropriate exercise sessionsfor the user. The user's experience using the computing device and/orexercise machine 100 may be enhanced, thereby improving technology.

In some embodiments, the processing device may transmit a notificationfor presentation on the computing device 12. The notification mayinclude an indication that an exercise performed by the user also helpsto achieve a second physical activity goal. For example, the user may beperforming a leg press exercise using the exercise machine 100, and theleg press exercise may have been selected because it improves a strengthlevel of attainment; however, the leg press exercise may also improvemobility, and thus, the notification may be presented on the computingdevice 12 to indicate the same. Further, the notification can indicatethat improving mobility may help the user achieve a physical activitygoal of jogging a mile, playing with their grandchildren, or anysuitable physical goal associated with the mobility level of attainment.

In some embodiments, while the user performs the exercise plan using theexercise machine 100, the processing device may monitor one or morecharacteristics of the user, performance measurements of the user,user-reported pain feedback, and the like. The processing device maydetermine whether an exercise in the set of exercises results in adesired outcome. The processing device may determine an exercise issuccessful if the user exceeds a performance measurement threshold,completes the exercise to a certain threshold percentage, reports theyare not experiencing pain, or the like. The artificial intelligenceengine 65 may generate one or more machine learning models 60 that aretrained to generate improved exercise plans based on whether theexercise in the set of exercises results in the desired outcome.Accordingly, the processing device may implement a feedback look toiteratively improve the generated exercise plans according to whetherthey are providing desired results.

FIG. 43 illustrates an example method 4300 for selecting a multimediaclip for a user based on data pertaining to the user. The method 4300may be performed by processing logic that may include hardware(circuitry, dedicated logic, etc.), firmware, software, or a combinationof them. The method 4300 and/or each of their individual functions,subroutines, or operations may be performed by one or more processingdevices of a control system (e.g., cloud-based computing system 16,computing device 12 of FIG. 1) implementing the method 4300. The method4300 may be implemented as computer instructions that are executable bya processing device (e.g., a computer-readable medium may be used tostore instructions that, when executed, cause a processor perform thefollowing steps or processes of the method 4300 ). In certainimplementations, the method 4300 may be performed by a single processingthread. Alternatively, the method 4300 may be performed by two or moreprocessing threads, each thread implementing one or more individualfunctions, routines, subroutines, or operations of the methods. Variousoperations of the method 4300 may be performed by one or more of thecloud-based computing system 16, the computing device 12, and/or thecomputing device 15 of FIG. 1.

At 4302, while the user performs the improved exercise plan, theprocessing device may receive data pertaining to the user. The datapertaining to the user may include one or more characteristics of theuser, performance measurements, sensor measurements, user-reporteddifficulty of an exercise, user-reported pain level, or some combinationthereof.

At 4304, the processing device may select, based on the data pertainingto the user, a multimedia clip from the data source 67, a website (e.g.,music video streaming website), a multimedia application (e.g., musicvideo streaming website), or any suitable source. In some embodiments,the artificial intelligence engine 65 may generate one or more machinelearning models 60 trained to select the multimedia clip based on thedata pertaining to the user. For example, the data pertaining to theuser may indicate the user is having a difficult time completing anexercise, and the machine learning model 60 may be trained to select amotivational audio clip to playback using the computing device 12 inreal-time or near real-time as the user performs the exercise. At 4306,while the user performs the improved exercise plan, the processingdevice cause, via the computing device 12, playback of the multimediaclip.

FIG. 44 illustrates an example method 4400 for determining, using one ormore machine learning models, at least one comorbidity for a user. Themethod 4400 may be performed by processing logic that may includehardware (circuitry, dedicated logic, etc.), firmware, software, or acombination of them. The method 4400 and/or each of their individualfunctions, subroutines, or operations may be performed by one or moreprocessing devices of a control system (e.g., cloud-based computingsystem 16, computing device 12 of FIG. 1) implementing the method 4400.The method 4400 may be implemented as computer instructions that areexecutable by a processing device (e.g., a computer-readable medium maybe used to store instructions that, when executed, cause a processorperform the following steps or processes of the method 4400 ). Incertain implementations, the method 4400 may be performed by a singleprocessing thread. Alternatively, the method 4400 may be performed bytwo or more processing threads, each thread implementing one or moreindividual functions, routines, subroutines, or operations of themethods. Various operations of the method 4400 may be performed by oneor more of the cloud-based computing system 16, the computing device 12,and/or the computing device 15 of FIG. 1.

At 4402, the processing device may execute the artificial intelligenceengine 65 to generate one or more machine learning models 60 trained todetermine one or more comorbidities for one or more users based on oneor more characteristics of the user one or more users. The machinelearning models 60 may be trained with training data that maps inputs tocorresponding target outputs. For example, the training data may mapcertain characteristics of users as inputs to comorbidities as outputs.The characteristics may include information pertaining to medicalhistories, familial medical histories, medical procedures, demographics,psychographics, physical, mental, emotional, cardiovascular,neurological, performance measurements, user-reported difficulties ofexercises, user-reported pain levels, and the like.

At 4404, the processing device may receive one or more characteristicsof a particular user. The processing device may input the one or morecharacteristics of the particular user into the trained machine learningmodel 60. The trained machined machine learning model 60 may use the oneor more characteristics of the user to determine at least onecomorbidity for the user. The processing device may cause a notificationto be presented on the user interface of the computing device 12. Insome embodiments, there are various resources (e.g., medical papers,medical journal, evidence-based guidelines) that are referenced by themachine learning models 60 when determining what the comorbidities ofthe user are. The resources may be curated by health professionals andapproved to be included in the data source 67. The data source 67 may bereferred to as a multi-disciplinary repository that includes resourcesand exercises curated from health professionals having differentbackgrounds, such as physical therapy, medicine, neurology, cardiology,psychology, etc. Thus, the data source 67 may be used as a single sourceto provide improved exercise plans and notifications to enable a personto improve their entire lifestyle (e.g., physical and mental).

FIG. 45 illustrates an example user interface 4500 presentinginformation pertaining to a control instruction 4502. The user interface4500 may be provided by the application 17 on the computing device 12.The control instruction 4502 may enable the presentation of anexplanation of what has occurred. The explanation may be in words, insymbols, in graphical elements, or in any other form or combinationthereof. For example, the explanation in FIG. 45 is depicted as stating“Received measurements from a sensor of the exercise device indicatethat a trigger condition is satisfied.” The measurements may beassociated with a force exerted on the one or more pedals, a revolutionper minute of the one or more pedals, a speed of rotation of the one ormore pedals, an angular moment of the one or more pedals, a range ofmotion of the one or more pedals, a radius of an arc traversed by theone or more pedals, or some combination thereof. One or more sensors mayobtain the one or more measurements, and the one or more sensors may beincluded in any suitable portion of the exercise device 100. Forexample, the one or more sensors may be include pressure sensors (e.g.,load cells), proximity sensors, temperature sensors, haptic sensors,piezoelectric sensors, electrical sensors, mechanical sensors, chemicalsensors, electromechanical sensors, electrochemical sensors ormechanicochemical sensors, etc. The measurements may be communicatedwirelessly to the cloud-based computing system 16 and/or the computingdevice 12. The measurements may be input into a machine learning model60 trained to generate the control instruction 4502 as output.

The control instruction 4502 also provides additional explanation, “Thefollowing control instruction has been transmitted to the exercisedevice: increase resistance provided by both pedals by X.” Thisadditional explanation indicates that the measurements received from theone or more sensors resulted in the resistance provided by both pedalsbeing increased by X (e.g., X may be a value, an amount, a percentage,etc.). The control instruction 4502 may also indicate that “Theresistance has been increased accordingly.” Presenting the controlinstruction 4502 on the user interface 4500 may provide clarity andenhance an understanding of the user as to why the resistance providedby the pedals has been changed.

Although resistance is shown as the operating parameter modified by thecontrol instruction 4502, any suitable operating parameter of the pedalsand/or motor may be modified, such as range of motion, revolutions perminute, speed, etc. Further, the operating parameters may be modifiedidentically, similarly or completely independently on different sides ofthe exercise device 100. Further, the pedals may include foot pedals,hand pedals, or some combination thereof.

The user interface 4500 may include various graphical elements 4504(e.g., buttons): a button associated with increasing the resistance, anda button associated with decreasing the resistance. The user may use aninput peripheral, such as a mouse, a keyboard, a microphone, atouchscreen, etc. to select one of the graphical elements 4504. Forexample, as depicted by a hand cursor, the user may select the button tocause the resistance to increase. The selection may be transmitted tothe cloud-based computing system 16 and/or the computing device 12. Theselection may cause a control instruction to be generated andtransmitted to the exercise device 100. The control instruction maycause an operating parameter associated with the resistance to increase.In some embodiments, when generating subsequent exercise sessions, theselection may cause one or more machine learning models 60 to beretrained to learn the user's preferences for resistance levels.

FIG. 46 illustrates an example method 4600 for transmitting a controlinstruction that causes the resistance of one or more pedals to bemodified. The method 4600 may be performed by processing logic that mayinclude hardware (circuitry, dedicated logic, etc.), firmware, software,or a combination of them. The method 4600 and/or each of its individualfunctions, subroutines, methods (e.g., object oriented programming), oroperations may be performed by one or more processing devices of acontrol system (e.g., cloud-based computing system 16, computing device12 of FIG. 1) implementing the method 4600. The method 4600 may beimplemented as computer instructions executable by a processing deviceof the control system (e.g., a computer-readable medium may be used tostore instructions that, when executed, cause a processor to perform thefollowing steps or processes of the method 4600). The method 4600 may beperformed in a similar manner to that of the method 2700. Variousoperations of the method 4600 may be performed by one or more of thecloud-based computing system 16, the computing device 12, the exercisemachine 100, and/or the computing device 15 of FIG. 1.

In some embodiments, one or more machine learning models 60 may begenerated and trained by the artificial intelligence engine 65 and/orthe training engine 50 to perform one or more of the operations of themethod 4600. For example, to perform the one or more operations, theprocessing device may execute the one or more machine learning models60. In some embodiments, the one or more machine learning models 60 maybe iteratively retrained to select different features capable ofenabling optimization of output. The features that may be modified mayinclude a number of nodes included in each layer of the machine learningmodels 60, an objective function executed at each node, a number oflayers, various weights associated with outputs of each node, and thelike.

The method 4600 may use the artificial intelligence engine 65 to modifyresistance of one or more pedals of an exercise device 100. The pedalsmay be foot pedals, hand pedals, or some combination thereof. At 4602,the processing device may generate, by the artificial intelligenceengine 65, a machine learning model 60 trained to receive one or moremeasurements as input. In some embodiments, the one or more measurementsmay be associated with a force exerted on the one or more pedals, arevolution per minute of the one or more pedals, a speed of rotation ofthe one or more pedals, an angular moment of the one or more pedals, arange of motion of the one or more pedals, a radius of an arc traversedby the one or more pedals, a degree of flexion, a degree of extension, askill level, or some combination thereof.

At 4604, the processing device may output, based on the one or moremeasurements, a control instruction that causes the exercise device 100to modify the resistance of the one or more pedals. In some embodiments,the control instruction may cause other operating parameters associatedwith the exercise device to be modified. For example, any combination ofthe resistance, range of motion, speed, revolutions per minute, etc. maybe modified by the control instruction.

At 4606, the processing device may receive the one or more measurementsfrom a sensor associated with the one or more pedals of the exercisedevice 100. As described herein, the sensor may include a pressuresensor including one or more load cells, proximity sensors, hapticsensors, piezoelectric sensors, optical sensors, temperature sensors,electrical sensors, mechanical sensors, chemical sensors,electromechanical sensors, electrochemical sensors or mechanicochemicalsensors or the like. There may be multiple sensors disposed in, on,around, or near the exercise device 100. The multiple sensors may beconfigured to obtain the one or more measurements. The sensors mayinclude a processing device, a memory device, a network interfacedevice, a sensing device, etc. The sensors may be configured towirelessly communicate the one or more measurements to the computingdevice 12 and/or the cloud-based computing system 16.

At 4608, the processing device may determine whether the one or moremeasurements satisfy a trigger condition. In some embodiments, thetrigger condition may include the one or more measurements being lessthan a threshold value, less than or equal to a threshold value, equalto a threshold value, more than or equal to a threshold value, or morethan a threshold value. For example, if a user is able to cycle at arange of motion of 50 degrees, and the trigger condition includes arange of motion being above 45 degrees, then the trigger condition maybe satisfied.

In some embodiments, the processing device may include receiving one ormore characteristics of a user operating the exercise device 100. Forexample, the one or more characteristics may include personalinformation, performance information, and measurement information. Thepersonal information may include, e.g., demographic, psychographic orother information, such as an age, a weight, a gender, a height, a bodymass index, a medical condition, a familial medication history, aninjury, a medical procedure, a medication prescribed, a comorbidity, orsome combination thereof. The performance information may include, e.g.,an elapsed time of using an exercise device, an amount of force exertedon a portion of the exercise device, a range of motion achieved on theexercise device, a duration of use of the exercise device, an indicationof a plurality of pain levels using the exercise device, or somecombination thereof. The measurement information may include, e.g., oneor more vital signs of the user, a respiration rate of the user, aheartrate of the user, a temperature of the user, an SpO2-measurement ofthe blood oxygen level of the user (e.g., oxygen saturation level), ablood pressure of the user, a glucose level of the user, other suitablemeasurement information of the user, microbiome related data pertainingto the user, or a combination thereof.

In some embodiments, based on the one or more characteristics of theuser and the one or more measurements, the processing device maydetermine whether the trigger condition has been satisfied. For example,if the user is over a certain age and the measurement indicates the userhas a certain heartrate, then the trigger condition may becomesatisfied.

At 4610, responsive to determining that the one or more measurementsand/or the one or more characteristics of the user satisfy the triggercondition, the processing device may transmit the control instruction tothe exercise device 100. In some embodiments, transmitting the controlinstruction to the exercise device 100 may cause the exercise device 100to modify the resistance of the one or more pedals in real-time or nearreal-time. In some embodiments, while a user uses the exercise device100 to perform an exercise (e.g., cycling), the resistance of the one ormore pedals may be modified. In some embodiments, the modification mayinclude modifying the resistance by the same degree, percentage oramount provided by a pedal on each side of the exercise device 100, ormodifying the resistance a different degree, percentage or amount ondifferent sides of the exercise device 100. In some embodiments, thecontrol instruction may be associated with changing an operatingparameter (e.g., speed, revolutions per minute) of a motor connected tothe one or more pedals of the exercise device 100.

In some embodiments, transmitting the control instruction to theexercise device 100 may cause the exercise device 100 to modify aparameter associated with a pedal on each side of the exercise device100 a same degree, percentage or amount or a different degree,percentage or amount. The parameter may include a range of motion,resistance, speed of a motor, revolutions per minute, or somecombination thereof.

In some embodiments, responsive to determining that the triggercondition has been satisfied, the processing device may modify anotheroperating parameter (e.g., different than resistance) of the exercisedevice 100. The another operating parameter may include a number ofrevolutions per minute, a speed value, a torque value, a temperaturedegree, a vibration level, or some combination thereof.

In some embodiments, the processing device may receive a second inputfrom the user. The second input may include an instruction to modify anoperating parameter of the exercise device 100. The second input may bereceived via a microphone, a touchscreen, a keyboard, a mouse, aproprioceptive sensor, or some combination thereof. For example, thesecond input may include a spoken voice from the user, wherein thespoken voice instructs the exercise device 100 to increase resistance.Accordingly, the processing device may use natural language processingto digitize an audio signal representing the spoken voice and to processthe digitized audio signal. Based on the digitized audio signal, theprocessing device may transmit a control instruction that causes theresistance provided by one or more of the pedals to be increased inreal-time or near real-time.

In some embodiments, the processing device may present the controlinstruction on a user interface of a computing device associated withthe exercise device 100. In some embodiments, the processing device mayreceive a selection from the computing device, and based on theselection, may modify the resistance of the one or more pedals, range ofmotion of the one or more pedals, speed of a motor, or some combinationthereof.

In some embodiments, the machine learning model 60 may be retrainedbased on the selection from the user because the selection may indicatea preference of the user, that the user is in pain, that the exercise istoo easy for the user, that the exercise is too hard for the user, orsome combination thereof. In some embodiments, the selection may causethe machine learning model 60 to select a different exercise to beimmediately performed in place of an exercise currently being performed,to select a different exercise to be subsequently performed in anexercise session, or both. In some embodiments, the selection may causethe artificial intelligence engine 65 to modify a feature of the machinelearning model 60, such that the machine learning model 60 accounts forthe selection when generating additional exercise sessions and/orexercise plans. For example, the feature may include a weight of a node,a number of nodes in a layer, a number of layers, or some combinationthereof. Such a technique may improve the exercises that are selected,such that they are based on input from the user.

FIG. 47 illustrates an example user interface 4700 presentinginformation pertaining to a notification 4702. The user interface 4700is presented by the application 17 on the computing device 12. A controlaction may be performed as a result of the computing device 12 and/orthe cloud-based computing 16 (e.g., machine learning models 60)determining that one or more measurements indicate, during an intervaltraining session, that one or more characteristics of a user iares notwithin or is within a desired target zone. The one or more measurementsmay be received from a wearable device worn by a user while the userperforms an interval training session. The one or more measurements mayinclude one or more vital signs of the user, a respiration rate of theuser, a heartrate of the user, a temperature of the user, anSpO2-measurement of the blood oxygen level of the user (e.g., oxygensaturation level), a blood pressure of the user, a perspiration rate ofthe user, a glucose level of the user, a revolutions per minute, anumber of steps, a speed, an amount of force, other suitable measurementinformation of the user, microbiome related data pertaining to the user,or a combination thereof. In some embodiments, the notification 4702 mayinclude feedback to encourage the user to perform, during the intervaltraining session, an exercise within the target training zone. Forexample, the depicted notification 4702 indicates “John Doe, yourheartrate is not in the desired target zone, pedal faster to increaseyour heartrate to within the desired target zone!”

The desired target zone for each user may be tailored based on the oneor more characteristics of the user. The one or more characteristics maypertain to personal information, performance information, and/ormeasurement information. The personal information may include, e.g.,demographic, psychographic or other information, such as an age, aweight, a gender, a height, a body mass index, a medical condition, afamilial medication history, an injury, a medical procedure, amedication prescribed, a comorbidity, or some combination thereof. Theperformance information may include, e.g., an elapsed time of using anexercise device, an amount of force exerted on a portion of the exercisedevice, a range of motion achieved on the exercise device, a duration ofuse of the exercise device, an indication of a plurality of pain levelsusing the exercise device, or some combination thereof. The measurementinformation may include, e.g., one or more vital signs of the user, arespiration rate of the user, a heartrate of the user, a temperature ofthe user, an SpO2-measurement of the blood oxygen level of the user(e.g., oxygen saturation level), a blood pressure of the user, a glucoselevel of the user, other suitable measurement information of the user,microbiome related data pertaining to the user, a perspiration rate, orsome combination thereof.

Further, the desired target zone may be based on a physical activitygoal specified by the user. For example, the user may have selected tobe able to play with their grandchildren, and the desired target zonemay be tailored according to one or more aspects (e.g., domainsincluding flexibility, strength, endurance, etc.) associated with theselected physical activity goal and/or the characteristics of the user.

The user interface 4700 may include various graphical elements 4704(e.g., buttons): a button associated with increasing the resistance, anda button associated with decreasing the resistance. The user may use aninput peripheral, such as a mouse, a keyboard, a microphone, atouchscreen, a virtual or augmented reality input device, etc. to selectone of the graphical elements 4704. For example, as depicted by a handcursor, the user may select the button to cause the resistance toincrease. The selection may be transmitted to the cloud-based computingsystem 16 and/or the computing device 12. The selection may cause acontrol instruction to be generated and transmitted to the exercisedevice 100. The control instruction may cause an operating parameterassociated with the resistance to increase. In some embodiments, theselection may cause one or more machine learning models 60 to beretrained to learn the user's preferences for resistance levels whengenerating subsequent exercise sessions.

FIG. 48 illustrates an example method 4800 for transmitting a controlinstruction that causes a control action to be performed based on one ormore measurements from a wearable device. The method 4800 may beperformed by processing logic that may include hardware (circuitry,dedicated logic, etc.), firmware, software, or a combination of them.The method 4800 and/or each of its individual functions, subroutines,methods (e.g., object oriented programming), or operations may beperformed by one or more processing devices of a control system (e.g.,cloud-based computing system 16, computing device 12 of FIG. 1)implementing the method 4800. The method 4800 may be implemented ascomputer instructions executable by a processing device of the controlsystem (e.g., a computer-readable medium may be used to storeinstructions that, when executed, cause a processor to perform thefollowing steps or processes of the method 4800 ). The method 4800 maybe performed in similar manner to that of the method 2700. Variousoperations of the method 4800 may be performed by one or more of thecloud-based computing system 16, the computing device 12, the exercisemachine 100, and/or the computing device 15 of FIG. 1.

In some embodiments, one or more machine learning models 60 may begenerated and trained by the artificial intelligence engine 65 and/orthe training engine 50 to perform one or more of the operations of themethod 4800. For example, to perform the one or more operations, theprocessing device may execute the one or more machine learning models60. In some embodiments, the one or more machine learning models 60 maybe iteratively retrained to select different features capable ofenabling the optimization of output. The features that may be modifiedmay include a number of nodes included in each layer of the machinelearning models 60, an objective function executed at each node, anumber of layers, various weights associated with outputs of each node,and the like.

The method 4800 may use the artificial intelligence engine 65 to performa control action. The control action may be based on one or moremeasurements from a wearable device or smart device (e.g., a watch, anecklace, an anklet, a bracelet, a belt, a ring, a hat, a shoe, a pieceof clothing, an earplug, etc.). The wearable device or smart device maybe worn by a user performing an exercise or while the user is notperforming the exercise and is stationary, relaxing, sleeping, or thelike.

At 4802, the processing device may generate, by the artificialintelligence engine 65, a machine learning model 60 trained to receivethe one or more measurements as input. In some embodiments, the one ormore measurements may include one or more vital signs of the user, arespiration rate of the user, a heartrate of the user, a temperature ofthe user, an SpO2-measurement of the blood oxygen level of the user(e.g., oxygen saturation level), a blood pressure of the user, a glucoselevel of the user, other suitable measurement information of the user,microbiome related data pertaining to the user, a perspiration rate, arevolutions per minute, a number of steps, a speed, an amount of force,or some combination thereof.

At 4804, the processing device may output, based on the one or moremeasurements, a control instruction that causes the control action to beperformed. In some embodiments, the control action may includetransmitting a notification for presentation on a user interface of acomputing device associated with the exercise device 100. Thenotification may include feedback to encourage the user to perform,during the interval training session, an exercise within the targettraining zone. In some embodiments, the control action may includecontrolling an operating parameter of the exercise device 100. Forexample, the control instruction may be received by a processing deviceof the exercise device 100, and based on the control instruction, theprocessing device may transmit a signal to control the operatingparameter (e.g., resistance, range of motion, speed, revolutions perminute, etc.).

At 4806, the processing device may receive the one or more measurementsfrom the wearable device being worn by a user. The one or moremeasurements may be received at a certain periodicity, on demand, orwhen certain trigger events occur, for example. The one or moremeasurements may be received during an interval training session. Insome embodiments, the interval training session may be included in anexercise plan associated with a rehabilitation program which the user isperforming. In some embodiments, the interval training session mayinclude short, high intensity bursts of activity with periods of restand recovery between. In other words, rest and exercise intervals ofcontrolled duration may be alternated during an interval trainingsession.

At 4808, the processing device may determine whether the one or moremeasurements indicate, during an interval training session, that one ormore characteristics of the user are within a desired target zone. Theone or more characteristics of the user may be associated with anactivity level determined based on physiological factors associated withthe one or more measurements. The physiological factors may includeheart rate, respiratory rate, perspiration rate, muscular state,readiness state, or the like. In some embodiments, the characteristic ofthe user may be associated with a physiological state (e.g., resting,active, hyperactive, etc.) based on the one or more measurements. Thecharacteristic of the user may be determined to be within the desiredtarget zone when a value, attribute, score, measure, property, etc.associated with the characteristic is within a certain range (e.g.,desired target zone). The certain range may be any numerical range,quantifiable range, quantitative range, etc.

In some embodiments, responsive to determining that the one or moremeasurements indicate, during the interval training session, that thecharacteristic of the user is within the desired target zone, performingthe control action, wherein the performing includes transmitting anotification to a computing device associated with an exercise device.The notification may provide a motivational message to the user.

At 4810, responsive to determining that the one or more measurementsindicate the characteristic of the user is not within the desired targetzone during the interval training session, the processing device mayperform the control action.

In some embodiments, the processing device may receive data associatedwith the user. In some embodiments, based on the data and the one ormore measurements, the processing device may predict, via the machinelearning model 60, a medical condition (e.g., hypertension, asthma,diabetes, etc.) associated with the user. For example, the machinelearning model 60 may be trained on a corpus of training data thatmatches patterns between certain user data and measurements associatedwith the presence (or absence) of certain medical conditions.

In some embodiments, the processing device may receive second input fromthe user. The second input may include an instruction to modify anoperating parameter of the exercise device 100. The second input may bereceived via a microphone, a touchscreen, a virtual or augmented realityinput device, a keyboard, a mouse, a proprioceptive sensor, or somecombination thereof. For example, the second input may include a spokenvoice from the user that instructs the exercise device 100 to increaseresistance. Accordingly, the processing device may use natural languageprocessing to digitize an audio signal representing the spoken voice andto process the digitized audio signal. Based on the digitized audiosignal, the processing device may transmit a control instruction thatcauses the resistance provided by one or more of the pedals to beincreased in real-time or near real-time.

FIG. 49 illustrates another example method 4900 for transmitting acontrol instruction that causes a control action to be performed basedon one or more measurements from a wearable device. The method 4900 maybe performed by processing logic that may include hardware (circuitry,dedicated logic, etc.), firmware, software, or a combination of them.The method 4900 and/or each of its individual functions, subroutines,methods (e.g., object oriented programming), or operations may beperformed by one or more processing devices of a control system (e.g.,cloud-based computing system 16, computing device 12 of FIG. 1)implementing the method 4900. The method 4900 may be implemented ascomputer instructions executable by a processing device of the controlsystem (e.g., a computer-readable medium may be used to storeinstructions that, when executed, cause a processor to perform thefollowing steps or processes of the method 4900). The method 4900 may beperformed in a manner similar to that of the method 2700. Variousoperations of the method 4900 may be performed by one or more of thecloud-based computing system 16, the computing device 12, the exercisemachine 100, and/or the computing device 15 of FIG. 1.

In some embodiments, one or more machine learning models 60 may begenerated and trained by the artificial intelligence engine 65 and/orthe training engine 50 to perform one or more of the operations of themethod 4900. For example, to perform the one or more operations, theprocessing device may execute the one or more machine learning models60. In some embodiments, the one or more machine learning models 60 maybe iteratively retrained to select different features capable ofenabling the optimization of output. The features that may be modifiedmay include a number of nodes included in each layer of the machinelearning models 60, an objective function executed at each node, anumber of layers, various weights associated with outputs of each node,and the like.

At 4902, the processing device may determine that the one or moremeasurements indicate the characteristic of the user is within anundesired target zone. In some embodiments, the undesired target zonemay include ranges for excessive heartrates, blood pressures,temperatures, and/or perspiration rates, for example.

At 4904, responsive to determining the one or more measurements indicatethe characteristic of the user is within the undesired target zone, theprocessing device may perform the control action that includestransmitting the control instruction to cause the exercise device 100 tostop operating, to slow down, to speed up, to generate a warning, orsome combination thereof. Further, the control action may includetransmitting a notification to be presented by the computing device 12,and the notification may include a warning, an alert, or the likeinstructing the user to stop exercising or slow down.

FIG. 50 illustrates an example method 5000 for independently controllingthe resistance provided by different pedals. The method 5000 may beperformed by processing logic that may include hardware (circuitry,dedicated logic, etc.), firmware, software, or a combination of them.The method 5000 and/or each of its individual functions, subroutines,methods (e.g., object oriented programming), or operations may beperformed by one or more processing devices of a control system (e.g.,cloud-based computing system 16, computing device 12 of FIG. 1)implementing the method 5000. The method 5000 may be implemented ascomputer instructions executable by a processing device of the controlsystem (e.g., a computer-readable medium may be used to storeinstructions that, when executed, cause a processor to perform thefollowing steps or processes of the method 5000). The method 5000 may beperformed in a manner similar to that of the method 2700. Variousoperations of the method 5000 may be performed by one or more of thecloud-based computing system 16, the computing device 12, the exercisemachine 100, and/or the computing device 15 of FIG. 1.

In some embodiments, one or more machine learning models 60 may begenerated and trained by the artificial intelligence engine 65 and/orthe training engine 50 to perform one or more of the operations of themethod 5000. For example, to perform the one or more operations, theprocessing device may execute the one or more machine learning models60. In some embodiments, the one or more machine learning models 60 maybe iteratively retrained to select different features capable ofenabling the optimization of output. The features that may be modifiedmay include a number of nodes included in each layer of the machinelearning models 60, an objective function executed at each node, anumber of layers, various weights associated with outputs of each node,and the like.

At 5002, the processing device may generate, by the artificialintelligence engine 65, a machine learning model 60 trained to receiveone or more measurements as input. In some embodiments, the one or moremeasurements are associated with a force exerted on the one or morepedals, a revolution per minute of the one or more pedals, a speed ofrotation of the one or more pedals, an angular moment of the one or morepedals, a range of motion of the one or more pedals, a radius of an arctraversed by the one or more pedals, a degree of flexion, a degree ofextension, a skill level, or some combination thereof.

At 5004, the processing device may output, based on the one or moremeasurements, a control instruction that causes the exercise device tomodify, independently from each other, the resistance of the one or morepedals. The control instruction may cause modification of anycombination of operating parameters, such as the resistance, range ofmotion, speed, revolutions per minute, etc. The control instruction mayinclude a value for configuring the operating parameter of the exercisedevice 100. The control instruction may be received by a processingdevice of the exercise device 100, and the processing device maytransmit a control signal to appropriate circuitry (e.g., motor, pedal,actuator, etc.) to control the operating parameter of the exercisedevice 100.

At 5006, while a user performs an exercise using the exercise device,the processing device may receive the one or more measurements from oneor more sensors associated with the one or more pedals of the exercisedevice 100. As described herein, the sensor may include a pressuresensor including one or more load cells, proximity sensors, hapticsensors, piezoelectric sensors, optical sensors, temperature sensors,electrical sensors, mechanical sensors, chemical sensors,electromechanical sensors, electrochemical sensors or mechanicochemicalsensors, or the like. There may be multiple sensors disposed in, on,around, or near the exercise device 100. The multiple sensors may beconfigured to obtain the one or more measurements. The sensors mayinclude a processing device, a memory device, a network interfacedevice, a sensing device, etc. The sensors may be configured to wirelesscommunicate the one or more measurements to the computing device 12and/or the cloud-based computing system 16.

At 5008, the processing device may determine, based on the one or moremeasurements, a quantifiable or qualitative modification to theresistance provided by a pedal of the one or more pedals. In someembodiments, the quantifiable or qualitative modification may include aspecific force (e.g., newtons) or pounds of resistance to be provided bythe pedal of the one or more pedals. In some embodiments, the resistanceprovided by another pedal of the one or more pedals is not modified. Insome embodiments, range of motion for one pedal may be modifiedindependently from the range of motion for another pedal. In someembodiments, revolutions per minute for one pedal may be modifiedindependently than the revolutions per minute for another pedal. In someembodiments, any combination of operating parameters (e.g. resistance,range of motion, revolutions per minute, etc.) may be modifiedindependently for different pedals. In some embodiments, the pedals mayinclude hand pedals, foot pedals, or some combination thereof.

At 5010, the processing device may transmit the control instruction tothe exercise device 100 to cause the resistance provided by the pedal tobe modified. In some embodiments, the control instruction mayautomatically cause the resistance provided by the pedal to be modifiedin real-time or near real-time. In some embodiments, the pedal may beactuated by an affected limb, and the affected limb is associated withrehabilitation, prehabilitation, post-habilitation, or some combinationthereof. In some embodiments, the another pedal of the one or morepedals may be actuated by a second limb.

In some embodiments, the processing device may include presenting anotification on a user interface of a computing device associated withthe user and/or the exercise device 100. The notification may include aprompt to modify the resistance provided by the pedal. In someembodiments, the processing device may cause an audio device (e.g.,speaker) to generate a notification. The notification may include aprompt (e.g., auditory, visual, haptic, etc.) to modify the resistanceprovided by the pedal, a range of motion provided by the pedal, a speedof the pedal, a revolutions per minute of the pedal, etc.

In some embodiments, the processing device may receive one or moresubsequent measurements from the one or more sensors. The processingdevice may determine whether the one or more subsequent measurementsindicate at least one of the at least two strength characteristic levelsfor the at least two limbs of the user. Responsive to determining thatthe one or more subsequent measurements indicate at least one of the atleast two strength characteristic levels for the at least two limbs, theprocessing device may modify a value of resistance to be provided by theone or more pedals.

In some embodiments, the processing device may receive a second inputfrom the user. The second input may include an instruction to modify anoperating parameter of the exercise device 100. The second input may bereceived via a microphone, a touchscreen, a keyboard, a mouse, aproprioceptive sensor, or some combination thereof. For example, thesecond input may include a spoken voice, wherein the spoken voiceinstructs the exercise device 100 to increase resistance. Accordingly,the processing device may use natural language processing to digitize anaudio signal representing the spoken voice and to process the digitizedaudio signal. Based on the digitized audio signal, the processing devicemay transmit a control instruction that causes the resistance providedby one or more of the pedals to be increased in real-time or nearreal-time.

FIG. 51 illustrates an example method 5100 for modifying, based on ameasured strength characteristic level of a limb of user, the resistanceprovided by one or more pedals. The method 5100 may be performed byprocessing logic that may include hardware (circuitry, dedicated logic,etc.), firmware, software, or a combination of them. The method 5100and/or each of its individual functions, subroutines, methods (e.g.,object oriented programming), or operations may be performed by one ormore processing devices of a control system (e.g., cloud-based computingsystem 16, computing device 12 of FIG. 1) implementing the method 5100.The method 5100 may be implemented as computer instructions executableby a processing device of the control system (e.g., a computer-readablemedium may be used to store instructions that, when executed, cause aprocessor perform the following steps or processes of the method 5100).The method 5100 may be performed in a manner similar to that of themethod 2700. Various operations of the method 5100 may be performed byone or more of the cloud-based computing system 16, the computing device12, the exercise machine 100, and/or the computing device 15 of FIG. 1.

In some embodiments, one or more machine learning models 60 may begenerated and trained by the artificial intelligence engine 65 and/orthe training engine 50 to perform one or more of the operations of themethod 5100. For example, to perform the one or more operations, theprocessing device may execute the one or more machine learning models60. In some embodiments, the one or more machine learning models 60 maybe iteratively retrained to select different features capable ofenabling the optimization of output. The features that may be modifiedmay include a number of nodes included in each layer of the machinelearning models 60, an objective function executed at each node, anumber of layers, various weights associated with outputs of each node,and the like.

At 5102, the processing device may receive one or more subsequentmeasurements from the one or more sensors. At 5104, the processingdevice may determine whether the one or more subsequent measurementsindicate at least two strength characteristic levels for at least twolimbs of the user. The at least two strength characteristic levels maybe associated with an amount of force able to be exerted by a first limbon a first pedal and an amount of force able to be exerted by a secondlimb on a second pedal. For example, the strength characteristic levelmay be configured to be 100 pounds of force. If the force exerted byeach of the first and second limb on the first and second pedal is equalto or greater than 100 pounds of force, then the first and second limbachieve the strength characteristic level. In such an instance, anaffected limb may have recovered to be as strong as the other unaffectedlimb.

At 5106, responsive to determining that the one or more subsequentmeasurements indicate at least one of the at least two strengthcharacteristic levels for the at least two limbs, the processing devicemay modify the resistance to be provided by the one or more pedals. Forexample, if the affected limb actuates a pedal that generatesmeasurements indicating the affected limb has achieved the strengthcharacteristic level, then the affected limb may have recovered to asufficient degree. Accordingly, the resistance provided by the pedalassociated with the affected limb may be adjusted or modified similarlyto the resistance provided by a pedal associated with the unaffected orother limb.

FIG. 52 illustrates an example user interface 5200 presenting respectivegraphical elements 5204, 5206, 5208, 5210, 5212, 5214, and 5216 fordifferent domains associated with a user. The user interface 5200 ispresented by the application 17 on the computing device 12. As depicted,John Doe is performing “Exercise Plan X” ( 5202 ) and the exercise plancurrently instructs John Doe to “Perform cycling exercise”. There arefive domains presented in the user interface 5200. The domains areassociated with health and/or fitness characteristics desired by theuser for a certain physical activity goal. The domains may be associatedwith range of motion, strength, balance, endurance, mobility, stability,pliability, flexibility, or some combination thereof. Each of thedomains may be presented in a respective section 5222, 5224, 5226, 5228,5230, 5232, and 5234 of the user interface 5200. The domains may includeDomain 1: Range of Motion; Domain 2; Strength; Domain 3: Balance; Domain4: Endurance; Domain 5: Mobility; Domain 6: Stability; and Domain 7:Flexibility. In some embodiments, there may be any suitable numberand/or combination of domains that are included in an exercise planbased on a desired physical activity goal.

Each domain is associated with a respective graphical element, and asdepicted, the graphical element represents a side of a mountain. Anysuitable graphical element may be used (e.g., a rope climb, a road, abuilding, a river, etc.). An icon 5232 (e.g., represented as a circle)may be presented relative to the graphical element 5204. The position ofthe icon 5232 relative to the graphical element 5204 may represent aprogress of the user with regard to the specific domain. For example,when the icon 5232 is located at a bottom of the graphical element 5204,then the user has not made any progress toward that particular domainassociated with the graphical element 5204. However, if the icon islocated at a top of the graphical element, then the user has completedprogress toward that particular domain associated with the graphicalelement 5204. As depicted, with regard to the graphical element 5204associated with the Range of Motion domain, the icon 5232 may move inreal-time (as depicted by the dashed circle moving, via the dashed line,to the enclosed circle) as the user performs an exercise, such ascycling, and the user increases progress towards a target goal ordesired completion state. As further, depicted by graphical element 5210associated with the Endurance domain, the icon has advanced in real-timeas the user performs the cycling exercise. Accordingly, as the userperforms various exercises, any suitable number of domains may bemodified to reflect the user's progress accurately.

As depicted, the user may select (e.g., via hand cursor), a particulargraphical element 5204 associated with a domain to drill down to viewmore detailed information pertaining to that domain (e.g., Range ofMotion). Accordingly, FIG. 53 illustrates an example user interface 5300presenting detailed information of a domain selected from FIG. 52. Theuser interface 5300 is presented by the application 17 on the computingdevice 12. As depicted, additional information 5302 may include “Rangeof Motion (ROM) Details”. The details may include “Beginning ROM: X,”“Current ROM: Y,” and “Projected Amount of Time Before Target ROM: 1week.” Accordingly, the user may be presented with informationindicating an amount of progress the user has made with regard to theselected domain. A machine learning model 60 may be trained to analyzethe progress the user has made, and based on the analysis, project anamount of time before a target ROM is achieved.

FIG. 54 illustrates an example method 5400 for dynamically moving, basedon one or more measurements associated with a user, graphical elementsin sections associated with domains. The method 5400 may be performed byprocessing logic that may include hardware (circuitry, dedicated logic,etc.), firmware, software, or a combination of them. The method 5400and/or each of its individual functions, subroutines, methods (e.g.,object oriented programming), or operations may be performed by one ormore processing devices of a control system (e.g., cloud-based computingsystem 16, computing device 12 of FIG. 1) implementing the method 5400.The method 5400 may be implemented as computer instructions executableby a processing device of the control system (e.g., a computer-readablemedium may be used to store instructions that, when executed, cause aprocessor perform to the following steps or processes of the method5400). The method 5400 may be performed in a manner similar to that ofthe method 2700. Various operations of the method 5400 may be performedby one or more of the cloud-based computing system 16, the computingdevice 12, the exercise machine 100, and/or the computing device 15 ofFIG. 1.

In some embodiments, one or more machine learning models 60 may begenerated and trained by the artificial intelligence engine 65 and/orthe training engine 50 to perform one or more of the operations of themethod 5400. For example, to perform the one or more operations, theprocessing device may execute the one or more machine learning models60. In some embodiments, the one or more machine learning models 60 maybe iteratively retrained to select different features capable ofenabling the optimization of output. The features that may be modifiedmay include a number of nodes included in each layer of the machinelearning models 60, an objective function executed at each node, anumber of layers, various weights associated with outputs of each node,and the like.

The method 5400 may use the artificial intelligence engine 65 to presenta user interface capable of presenting the progress of a user in one ormore domains. At 5402, the processing device may generate, by theartificial intelligence engine 65, a machine learning model 60 trainedto receive one or more measurements as input. In some embodiments, theone or more measurements may be associated with a force exerted on theone or more pedals, a revolution per minute of the one or more pedals, aspeed of rotation of the one or more pedals, an angular moment of theone or more pedals, a range of motion of the one or more pedals, aradius of an arc traversed by the one or more pedals, a degree offlexion, a degree of extension, a skill level, or some combinationthereof.

At 5404, the processing device may output, based on the one or moremeasurements, a user interface that causes one or more icons todynamically change position on the user interface. The icons mayrepresent an amount of progress the user has made in a respective domainassociated with an exercise plan. Each domain may have a target progressgoal toward which the user is striving. The target progress goal may berepresented by a number, a value, a percentage, a state, etc.

At 5406, while a user performs an exercise using the exercise device,the processing device may receive the one or more measurements from oneor more sensors associated with the exercise device 100. As describedherein, the one or more sensors may include a pressure sensor includingone or more load cells, proximity sensors, haptic sensors, piezoelectricsensors, optical sensors, temperature sensors, electrical sensors,mechanical sensors, chemical sensors, electromechanical sensors,electrochemical sensors or mechanicochemical sensors or the like. Theremay be multiple sensors disposed in, on, around, or near the exercisedevice 100. The multiple sensors may be configured to obtain the one ormore measurements. The sensors may include a processing device, a memorydevice, a network interface device, a sensing device, etc. The sensorsmay be configured to wireless communicate the one or more measurementsto the computing device 12 and/or the cloud-based computing system 16.

At 5408, the processing device may present, on a computing deviceassociated with the exercise device 100, one or more sections of theuser interface. The one or more sections may include independentgraphical elements, objects, representations, or the like. The one ormore sections may include overlapping graphical elements, objects,representations, or the like. In some embodiments, one section may sharean aspect (e.g., border, graphical element, region, object, shape,representation, text, etc.) with another section. The one or moresections may each include respective text, graphical elements, colors,and the like. The one or more sections may be each related to a separatedomain including the one or more domains and wherein, based on the oneor more measurements, each section may include the one or more iconsplaced. In some embodiments, the processing device may present each ofthe one or more sections as portions of a mount on the user interface.The portions may include parts, subsections, sections, portions,segments, or the like.

In some embodiments, the processing device may predict, based on theprogress the user has made in each of the one or more domains, acompletion date for an exercise plan. The predicting may be performed bythe processing device executing a machine learning model 60 trained toreceive the progress and output the predicted completion date. Forexample, the machine learning model 60 may be trained using a corpus oftraining data that includes inputs of various progression as measuredwith respect to the domains matched to completion statistics of theexercise plan.

In some embodiments, the processing device may receive, via an inputperipheral, a selection of a domain, wherein the selection includes theone or more domains. In some embodiments, the processing device maypresent additional information related to the progress of the userassociated with the selected domain. In some embodiments, the additionalinformation may include characteristics, details, attributes,properties, parameters, values, descriptions, identifiers, and the like.In some embodiments, the one or more domains may include range ofmotion, strength, balance, endurance, mobility, stability, pliability,flexibility, pain, or some combination thereof.

In some embodiments, the processing device may receive a second inputfrom the user. The second input may include an instruction to modify anoperating parameter of the exercise device 100. The second input may bereceived via a microphone, a touchscreen, a keyboard, a mouse, aproprioceptive sensor, or some combination thereof. For example, thesecond input may include a spoken voice from the user that instructs theexercise device 100 to increase resistance. Accordingly, the processingdevice may use natural language processing to digitize an audio signalrepresenting the spoken voice and process the digitized audio signal.Based on the digitized audio signal, the processing device may transmita control instruction that causes the resistance provided by one or moreof the pedals to be increased in real-time or near real-time.

FIG. 55 illustrates an example method 5500 for modifying an exerciseplan based on adjustment made by a user. The method 5500 may beperformed by processing logic that may include hardware (circuitry,dedicated logic, etc.), firmware, software, or a combination of them.The method 5500 and/or each of its individual functions, subroutines,methods (e.g., object oriented programming), or operations may beperformed by one or more processing devices of a control system (e.g.,cloud-based computing system 16, computing device 12 of FIG. 1)implementing the method 5500. The method 5500 may be implemented ascomputer instructions executable by a processing device of the controlsystem (e.g., a computer-readable medium may be used to storeinstructions that, when executed, cause a processor to perform thefollowing steps or processes of the method 5500). The method 5500 may beperformed in a manner similar to that of the method 2700. Variousoperations of the method 5500 may be performed by one or more of thecloud-based computing system 16, the computing device 12, the exercisemachine 100, and/or the computing device 15 of FIG. 1.

In some embodiments, one or more machine learning models 60 may begenerated and trained by the artificial intelligence engine 65 and/orthe training engine 50 to perform one or more of the operations of themethod 5500. For example, to perform the one or more operations, theprocessing device may execute the one or more machine learning models60. In some embodiments, the one or more machine learning models 60 maybe iteratively retrained to select different features capable ofenabling the optimization of output. The features that may be modifiedmay include a number of nodes included in each layer of the machinelearning models 60, an objective function executed at each node, anumber of layers, various weights associated with outputs of each node,and the like.

At 5502, the processing device may receive, from the computing deviceassociated with the user, an adjustment to a value associated with adomain comprising the one or more domains. That is, the user is enabledto configure their progress in any of the domains as they desire. Forexample, the user may increase their completion progress toward a rangeof motion goal in the domain associated with range of motion.

At 5504, based on the adjustment to the value, the processing device mayselect, by the machine learning model 60, one or more exercises toinclude in an exercise plan for the user. Continuing the above example,since the user increased their completion progress toward the range ofmotion goal, the machine learning model 60 may select exercises withranges of motion increased in relation to the completion progressindicated by the user.

At 5506, the processing device may determine a completion state of anexercise including the one or more exercises. The completion state maybe determined based on a metric associated with the exercise (e.g.,elapsed time, distance traveled, number of repetitions completed, numberof sets completed, range of motion achieved, force achieved, speedachieved, etc.). The completion state may be represented by apercentage, an amount, a binary value (e.g., 1 or 0), or any suitableindicator.

At 5508, based on the completion state, the processing device may modifya difficulty level of the exercise. For example, if the completion stateindicates the completion state for the exercise is below a thresholdcompletion level, then the difficulty for the exercise may be reduced.Continuing the above example, if the completion state indicates the useris not able to sufficiently complete the exercise having the range ofmotion increased by the user, then the difficulty may be decreased forthat exercise (e.g., the range of motion may be decreased back to aprior value describing a past progress of the user in the domainassociated with range of motion). If the completion state indicates thecompletion state for the exercise is above the threshold completionlevel, then the difficulty for the exercise may be maintained orincreased. The machine learning model may be retrained to selectexercises having the increased range of motion and/or an increaseddifficulty level.

FIG. 56 illustrates an example user interface 5600 presenting ananimated virtual character 5602 performing a movement as a result ofuser input. The input may be received via any suitable input peripheral,such as a microphone, a keyboard, a mouse, a touchscreen, etc. Forexample, the user may say “increase resistance” and the microphone mayreceive the audio signal, wherein the audio signal may include thespoken words. A processing device may be configured to transform theaudio signal into a digitized audio signal that can be processed byusing natural language processing. The processing device (e.g., of thecomputing device 12 and/or the cloud-based computing system 16) mayprocess text included in the audio and generate a control instructionthat causes the resistance provided by the pedals of the exercise device100 to be increased.

The virtual character 5602 may be animated to graphically move and toaudibly generate a motivational quote 5604 to the user. In someembodiments, the virtual character 5602 may include a virtual coach asdescribed further herein. In some embodiments, the virtual character5602 may include a live-person performing an exercise in real-time. Insome embodiments, the virtual character 5602 may include a prerecordingof a live-person performing an exercise.

The user interface 5600 may include various graphical elements 5606(e.g., buttons): a button associated with increasing the resistance, anda button associated with decreasing the resistance. The user may use aninput peripheral, such as a mouse, a keyboard, a microphone, atouchscreen, etc. to select one of the graphical elements 5606. Forexample, as depicted by a hand cursor, the user may select the button tocause the resistance to increase. The selection may be transmitted tothe cloud-based computing system 16 and/or the computing device 12. Theselection may cause a control instruction to be generated andtransmitted to the exercise device 100. The control instruction maycause an operating parameter associated with the resistance to increase.In some embodiments, when generating subsequent exercise sessions, theselection may cause one or more machine learning models 60 to beretrained to learn the user's preferences for resistance levels.

FIG. 57 illustrates an example method 5700 for determining an output tocontrol an aspect of an exercise bike based on input received from theuser. The method 5700 may be performed by processing logic that mayinclude hardware (circuitry, dedicated logic, etc.), firmware, software,or a combination of them. The method 5700 and/or each of its individualfunctions, subroutines, methods (e.g., object oriented programming), oroperations may be performed by one or more processing devices of acontrol system (e.g., cloud-based computing system 16, computing device12 of FIG. 1) implementing the method 5700. The method 5700 may beimplemented as computer instructions executable by a processing deviceof the control system (e.g., a computer-readable medium may be used tostore instructions that, when executed, cause a processor to perform thefollowing steps or processes of the method 5700 ). The method 5700 maybe performed in a manner similar to that of the method 2700. Variousoperations of the method 5700 may be performed by one or more of thecloud-based computing system 16, the computing device 12, the exercisemachine 100, and/or the computing device 15 of FIG. 1.

In some embodiments, one or more machine learning models 60 may begenerated and trained by the artificial intelligence engine 65 and/orthe training engine 50 to perform one or more of the operations of themethod 5700. For example, to perform the one or more operations, theprocessing device may execute the one or more machine learning models60. In some embodiments, the one or more machine learning models 60 maybe iteratively retrained to select different features capable ofenabling the optimization of output. The features that may be modifiedmay include a number of nodes included in each layer of the machinelearning models 60, an objective function executed at each node, anumber of layers, various weights associated with outputs of each node,and the like.

During an exercise session, the method 5700 may use the artificialintelligence engine 65 to interact with a user of an exercise device100. At 5702, the processing device may generate, by the artificialintelligence engine 65, a machine learning model 60 trained to receivedata as input. In some embodiments, the data may include an electronicrecording of a voice of the user received via a microphone associatedwith the computing device. In some embodiments, the data may beassociated with a difficulty of an exercise the user is currentlyperforming, and the processing device may modify an exercise plan basedon the data. In some embodiments, the data may include an indicationfrom the user that an exercise is too difficult, and the machinelearning model may be trained to select a less difficult exercise forthe user in a subsequent exercise session.

At 5704, based on the data, the processing device may generate anoutput. In some embodiments, the output may include a virtual characteranimated to graphically move and to audibly generate a motivationalquote to the user. In some embodiments, the output may include a controlinstruction that causes the exercise device 100 to change an operatingparameter. In some embodiments, the operating parameter may includechanging a range of motion of one or more pedals, changing a speed of amotor, changing a revolutions per minute of a motor, changing aparameter of a fan associated with the exercise bike, changing atemperature of a portion of the exercise bike, changing a haptic settingof a portion of the exercise bike, or some combination thereof.

At 5706, while a user performs an exercise using the exercise device100, the processing device may receive the data from an input peripheralof a computing device 12 associated with a user. The input peripheralmay include a microphone, a keyboard, a mouse, a touchscreen, etc.

At 5708, based on the data being received from the input peripheral, theprocessing device may determine, via the machine learning model 60, theoutput to control an aspect of the exercise device 100. An “aspect” mayrefer to a characteristic, an attribute, a property, a part,information, detail, a controlling mechanism, or some combinationthereof. For example, the aspect may refer to controlling an amount ofresistance provided by one or more pedals, a range of motion provided byone or more pedals, a speed provided by one or more pedals and/or amotor, a revolutions per minute of one or more pedals and/or a wheel,etc.

In some embodiments, the processing device may select a virtualcharacter, a prerecorded message, or both, to present via the computingdevice. The virtual character, the prerecorded message, or both mayinclude a motivational quote. The virtual character may be a virtualcoach, as described herein. Further, the virtual character may be alive-person performing an exercise in real-time. The virtual charactermay be a prerecorded live-person performing a an exercise.

In some embodiments, the processing device may receive second input fromthe user. The second input may include an instruction to modify anoperating parameter of the exercise device 100. The second input may bereceived via a microphone, a touchscreen, a keyboard, a mouse, aproprioceptive sensor, or some combination thereof. For example, thesecond input may include spoken voice from the user that instructs theexercise device 100 to increase resistance. Accordingly, the processingdevice may use natural language processing to digitize an audio signalrepresenting the spoken voice and process the digitized audio signal.Based on the digitized audio signal, the processing device may transmita control instruction that causes the resistance provided by one or moreof the pedals to be increased in real-time or near real-time.

FIG. 58 illustrates an example method 5800 for using an onboardingprotocol to generate an exercise plan for a user. The method 5800 may beperformed by processing logic that may include hardware (circuitry,dedicated logic, etc.), firmware, software, or a combination of them.The method 5800 and/or each of its individual functions, subroutines,methods (e.g., object oriented programming), or operations may beperformed by one or more processing devices of a control system (e.g.,cloud-based computing system 16, computing device 12 of FIG. 1)implementing the method 5800. The method 5800 may be implemented ascomputer instructions executable by a processing device of the controlsystem (e.g., a computer-readable medium may be used to storeinstructions that, when executed, cause a processor to perform thefollowing steps or processes of the method 5800). The method 5800 may beperformed in a manner similar to that of the method 2700. Variousoperations of the method 5800 may be performed by one or more of thecloud-based computing system 16, the computing device 12, the exercisemachine 100, and/or the computing device 15 of FIG. 1.

In some embodiments, one or more machine learning models 60 may begenerated and trained by the artificial intelligence engine 65 and/orthe training engine 50 to perform one or more of the operations of themethod 5800. For example, to perform the one or more operations, theprocessing device may execute the one or more machine learning models60. In some embodiments, the one or more machine learning models 60 maybe iteratively retrained to select different features capable ofenabling optimization of output. The features that may be modified mayinclude a number of nodes included in each layer of the machine learningmodels 60, an objective function executed at each node, a number oflayers, various weights associated with outputs of each node, and thelike.

The method 5800 may use the artificial intelligence engine 65 to onboarda user for an exercise plan. In some embodiments, onboarding may referto the fulfillment of a set of conditions that form a predicate neededto enable the user to start using the system. At 5802, the processingdevice may generate, by the artificial intelligence engine 65, a machinelearning model 60 trained to receive as input onboarding data associatedwith a user and an onboarding protocol and, based on the onboarding dataand the onboarding protocol, to output an exercise plan.

At 5804, while the user performs an exercise using the exercise device100, the processing device may receive the onboarding data associatedwith the user. The onboarding data may include one or more measurementsmay be associated with a force exerted on the one or more pedals, arevolution per minute of the one or more pedals, a speed of rotation ofthe one or more pedals, an angular moment of the one or more pedals, arange of motion of the one or more pedals, a radius of an arc traversedby the one or more pedals, a degree of flexion, a degree of extension, askill level, or some combination thereof. The onboarding data may alsoinclude an indication of a pain level the user experiences whileperforming an exercise. The onboarding data may include personalinformation associated with the user and/or performance informationassociated with the user.

At 5806, the processing device may determine, by the machine learningmodel 60 using the onboarding data and the onboarding protocol, afitness level of the user. The onboarding protocol may include exerciseswith tiered difficulty levels. The exercises may be associated with aplurality of domains comprising range of motion, strength, balance,endurance, mobility, stability, pliability, flexibility, pain, or somecombination thereof. When the user completes an exercise included in theexercises, the onboarding protocol may increase a difficulty level for asubsequent exercise included in the exercises. Based on a completionstate of a last exercise performed by the user, the fitness level of theuser may be determined.

At 5808, by associating the difficulty level for each exercise with thefitness level of the user, the processing device may select a difficultylevel for each exercise included in the exercise plan. In someembodiments, the machine learning model 60 may be trained to generatethe exercise plan to reduce a pain level of the user, a dependency ofthe user on a certain medication (preferably opioids or other addictivedrugs), or some combination thereof.

In some embodiments, as the user performs the exercise plan, theprocessing device may receive one or more measurements from one or moresensors, the computing device associated with the user and/or exercisedevice 100, or some combination thereof. Based on the one or moremeasurements, the processing device may modify a portion of the exerciseplan to include different exercises. For example, the portion of theexercise plan that is modified may include one exercise, two exercises,three exercises, or any suitable number of exercises. That is, anycombination of exercises may be modified in the exercise plan.Modification may refer to addition, change, deletion, etc. In someembodiments, the processing device may present the exercise plan on acomputing device associated with the exercise device 100.

In some embodiments, the processing device may receive feedback from theuser. The feedback may pertain to a pain level of the user, an enjoymentlevel of the user performing the exercise plan, or some combinationthereof. Based on the feedback, the processing device may modify theexercise plan to include different exercises.

In some embodiments, a virtual character may be presented on a computingdevice associated with the exercise device 100 or the user. The virtualcharacter may include a coach that provides instructions regarding howto properly perform an exercise. The virtual coach may be animated onthe user interface and may perform the proper movements associated withthe exercise. Further, the coach may speak and audio representing thevirtual coach's speech may be generated. The speech may includeinstructions regarding how to properly perform the exercise. The virtualcharacter may be a virtual avatar, a live person presented in real-time,a pre-recorded live person, etc.

In some embodiments, the processing device may receive input from theuser. The input may include an instruction to modify an operatingparameter of the exercise device 100. The input may be received via amicrophone, a touchscreen, a keyboard, a mouse, a proprioceptive sensor,or some combination thereof. For example, the input may include a spokenvoice from the user that instructs the exercise device 100 to increase aresistance. Accordingly, the processing device may use natural languageprocessing to digitize an audio signal representing the spoken voice andto process the digitized audio signal. Based on the digitized audiosignal, the processing device may transmit a control instruction thatcauses the resistance provided by one or more of the pedals to beincreased in real-time or near real-time.

The various aspects, embodiments, implementations or features of thedescribed embodiments can be used separately or in any combination. Theembodiments disclosed herein are modular in nature and can be used inconjunction with or coupled to other embodiments, including bothstatically-based and dynamically-based equipment. In addition, theembodiments disclosed herein can employ selected equipment such thatthey can identify individual users and auto-calibrate thresholdmultiple-of-body-weight targets, as well as other individualizedparameters, for individual users.

Consistent with the above disclosure, the examples of systems and methodenumerated in the following clauses are specifically contemplated andare intended as a non-limiting set of examples.

Clauses:

1. A computer-implemented method for using an artificial intelligenceengine to onboard a user for an exercise plan, wherein thecomputer-implemented method comprises:

generating, by the artificial intelligence engine, a machine learningmodel trained to receive as input onboarding data associated with a userand an onboarding protocol and, based on the onboarding data and theonboarding protocol, output an exercise plan;

while a user performs an exercise using the exercise device, receivingthe onboarding data associated with the user;

determining, by the machine learning model, wherein the machine learningmodel uses the onboarding data and the onboarding protocol, a fitnesslevel of the user, wherein:

the onboarding protocol comprises exercises with tiered difficultylevels,

the onboarding protocol increases a difficulty level for a subsequentexercise comprising the exercises when the user completes an exercisecomprising the exercises, and

based on a completion state of a last exercise performed by the user,the fitness level of the user is determined; and

by associating the difficulty level for each exercise with the fitnesslevel of the user, selecting a difficulty level for each exercisecomprising the exercise plan.

2. The computer-implemented method of any preceding clause, wherein theexercises are associated with a plurality of domains comprising range ofmotion, strength, balance, endurance, mobility, stability, pliability,flexibility, or some combination thereof.

3. The computer-implemented method of any preceding clause, furthercomprising:

as the user performs the exercise plan, receiving one or moremeasurements;

based on the one or more measurements, modifying a portion of theexercise plan to include different exercises.

4. The computer-implemented method of any preceding clause, furthercomprising:

receiving feedback from the user, wherein the feedback pertains to apain level of the user, an enjoyment level of the user performing theexercise plan, or some combination thereof; and

based on the feedback, modifying the exercise plan to include differentexercises.

5. The computer-implemented method of any preceding clause, furthercomprising:

presenting a virtual character on a computing device associated with theexercise device, wherein the virtual character comprises a coach thatprovides instructions as to how to properly perform an exercise.

6. The computer-implemented method of any preceding clause, wherein thecoach is a virtual avatar.

7. The computer-implemented method of any preceding clause, wherein thecoach is a live person presented in real-time.

8. The computer-implemented method of any preceding clause, wherein thecoach is a pre-recorded live person.

9. The computer-implemented method of any preceding clause, wherein themachine learning model is trained to generate the exercise plan toreduce a pain level of the user, a dependency of the user on a certainmedication, or some combination thereof.

10. The computer-implemented method of any preceding clause, furthercomprising presenting the exercise plan on a computing device associatedwith the exercise device.

11. The computer-implemented method of any preceding clause, furthercomprising:

receiving input from the user, wherein the input comprises aninstruction to modify an operating parameter of the exercise device, andthe input is received via a microphone, a touchscreen, a keyboard, amouse, a haptic signal, or some combination thereof.

12. A tangible, non-transitory computer-readable medium storinginstructions that, when executed, cause a processing device to:

generate, by an artificial intelligence engine, a machine learning modeltrained to receive as input onboarding data associated with a user andan onboarding protocol and, based on the onboarding data and theonboarding protocol, output an exercise plan;

while a user performs an exercise using an exercise device, receive theonboarding data associated with the user;

determine, by the machine learning model, wherein the machine learningmodel uses the onboarding data and the onboarding protocol, a fitnesslevel of the user, wherein:

the onboarding protocol comprises exercises with tiered difficultylevels,

the onboarding protocol increases a difficulty level for a subsequentexercise comprising the exercises when the user completes an exercisecomprising the exercises, and

based on a completion state of a last exercise performed by the user,the fitness level of the user is determined; and

by associating the difficulty level for each exercise with the fitnesslevel of the user, select a difficulty level for each exercisecomprising the exercise plan.

13. The computer-readable medium of any preceding clause, wherein theexercises are associated with a plurality of domains comprising range ofmotion, strength, balance, endurance, mobility, stability, pliability,flexibility, or some combination thereof.

14. The computer-readable medium of any preceding clause, wherein theprocessing device is configured to:

as the user performs the exercise plan, receive one or moremeasurements;

based on the one or more measurements, modifying a portion of theexercise plan to include different exercises.

15. The computer-readable medium of any preceding clause, wherein theprocessing device is configured to:

receive feedback from the user, wherein the feedback pertains to a painlevel of the user, an enjoyment level of the user performing theexercise plan, or some combination thereof; and

based on the feedback, modify the exercise plan to include differentexercises.

16. The computer-readable medium of any preceding clause, wherein theprocessing device is configured to:

present a virtual character on a computing device associated with theexercise device, wherein the virtual character comprises a coach thatprovides instructions as to how to properly perform an exercise.

17. The computer-readable medium of any preceding clause, wherein thecoach is a virtual avatar.

18. The computer-readable medium of any preceding clause, wherein thecoach is a live person presented in real-time.

19. The computer-implemented method of any preceding clause, wherein thecoach is a pre-recorded live person.

20. A system comprising:

A memory device storing instructions;

A processing device communicatively coupled to the memory device,wherein the processing device executes the instructions to:

generate, by an artificial intelligence engine, a machine learning modeltrained to receive as input onboarding data associated with a user andan onboarding protocol and, based on the onboarding data and theonboarding protocol, output an exercise plan;

while a user performs an exercise using an exercise device, receive theonboarding data associated with the user;

determine, by the machine learning model, wherein the machine learningmodel uses the onboarding data and the onboarding protocol, a fitnesslevel of the user, wherein:

the onboarding protocol comprises exercises with tiered difficultylevels,

the onboarding protocol increases a difficulty level for a subsequentexercise comprising the exercises when the user completes an exercisecomprising the exercises, and

based on a completion state of a last exercise performed by the user,the fitness level of the user is determined; and

by associating the difficulty level for each exercise with the fitnesslevel of the user, select a difficulty level for each exercisecomprising the exercise plan.

No part of the description in this application should be read asimplying that any particular element, step, or function is an essentialelement that must be included in the claim scope. The scope of patentedsubject matter is defined only by the claims. Moreover, none of theclaims is intended to invoke 35 U.S.C. § 112 (f) unless the exact words“means for” are followed by a participle.

The foregoing description, for purposes of explanation, used specificnomenclature to provide a thorough understanding of the describedembodiments. However, it should be apparent to one skilled in the artthat the specific details are not required in order to practice thedescribed embodiments. Thus, the foregoing descriptions of specificembodiments are presented for purposes of illustration and description.They are not intended to be exhaustive or to limit the describedembodiments to the precise forms disclosed. It should be apparent to oneof ordinary skill in the art that many modifications and variations arepossible in view of the above teachings.

The information below may provide a guideline of the rules employed indelivering an effective, well-structured exercise program for rehab,conditioning, and/or long-term fitness adapted to the capabilities ofeach user. The information below is for explanatory purposes and thesubject matter of the present disclosure is not limited to the examplesprovided below.

-   -   a) Initially a processing device (e.g., cloud-based computing        system 16, computing device 15, or both) may filter users into        their correct exercise levels.    -   b) Then the processing device may determine what combination of        exercises is appropriate for the users in beginning their        training.    -   c) As the users complete exercise sessions a machine learning        model may learn the user's ability and tracks their progress,        adjusts their exercises in exercise sessions accordingly, and        determines when the users can advance to the next exercise        level.

One objective may be to deliver the most effective exercise plan devisedfor each user, the exercise plan may be defined as one people actuallyengage in fully, frequently, and consistently.

2. Determining A User's Exercise Level

Exercise levels may range from 1 to 5. Placing a user in an exerciselevel may be a function of measuring the individual's Range-of-Motion(ROM) and establishing the Degree-of-Knee-Pain they are experiencing.

How the Combination of Pain and ROM Test Results Define Levels:

-   -   Pain—the level of pain as indicated by the user (they will enter        this into the system manually by typing or voice)    -   ROM—the result of their Range-of-Motion test

If Pain Is & ROM Is Then Level Is 8-10 1-2 1 8-10 3-4 2 8-10 5 3 5-7 1-2 1 5-7  3-4 2 5-7  5 3 1-4  1-2 1 1-4  3-4 2 1-4  5 4 0 5 5

How the Combination of Level and ROM Test Results Define Resistance:

If Level Is & ROM Is Resistance Is 1 1-2 Notch 1-2 2 3-4 Notch 3-4 3-5 5Notch 5

a) Measuring ROM

The User is instructed as follows:

-   -   1. Set both pedals of the exercise cycle to the lowest level of        movement required to turn the wheel a full revolution, i.e., the        smallest radius, ROM-Notch Setting 1 of 5.    -   2. Test both legs simultaneously as follows:        -   (a) Beginning at the lowest ROM setting on each pedal, pedal            for a maximum of one minute or until the onset of pain in            either knee.        -   (b) If there is no pain in either knee during the first            minute of pedalling, increase the radius setting of the            pedal by one-notch and pedal for an additional minute.        -   (c) Continue this process of simultaneously increasing the            ROM-Notch settings for the radius of each pedal by one notch            and pedaling for an additional minute until experiencing            knee pain.    -   3. If pedaling at a radius of 5 ROM Notches is pain free, record        the opening ROM Settings as 5 for each pedal.        -   (a) Otherwise, when pain is experienced in either knee, stop            pedaling, and            -   (i) record the opening ROM Notch Setting for the                affected leg as one notch less than the notch that                produced the pain, and            -   (ii) and set the pedal for the affected leg to the notch                recorded.    -   4. If pain is experienced at a ROM Notch Setting of 1 notch, or        if the degree of pain experienced is greater than a 3-intensity        on a scale of 10 at any notch, stop the ROM testing immediately,        refer to our “User Ramp-Up To Cycling” video, and follow its        instructions.    -   5. If the degree of pain is a 3-intensity or less continue the        ROM test by resuming pedaling, with the pedal for the affected        leg at the notch set in 3 (i) and continue the ROM test for the        other leg as follows:        -   (i) Increase the setting by one notch for the radius of the            pedal being used by the other leg and pedal for an            additional minute or until experiencing knee pain in the            other leg.        -   (ii) when pain is experienced, stop pedaling, and:            -   1. record the opening ROM Notch setting for the other                leg as one notch less than the notch that produced the                pain, and            -   2. and set the pedal for the other leg to the notch                recorded.        -   (iii) if pain is not experienced in the other leg, set the            pedal for the other leg to ROM Notch 5.

a) Establishing Degree of Pain

The processing device may ask (e.g., via the virtual coach (artificialintelligence driver) and displays to the user an array ofemoticons/terms reflecting various degrees of pain.

-   -   i. The user responds (e.g., by voice) or selects the one most        closely representing the user's degree of knee pain at the time        of making the selection.    -   ii. Users report (e.g., by voice) or enters onto the dashboard        their degree of pain each time they log in.

b) Tracking ROM and Degree of Knee Pain to Advance Through the ExerciseLevels

-   -   i. The status of ROM is tested and Degree of Knee Pain is        reported on a 5-Exercise Session interval, i.e., both are        updated every 6th session.        -   1. Every 5th login the system displays a notice and alerts            the User (e.g., by voice) that the next login will begin            with a ROM test.    -   ii. During the ROM test the system guides the user by the        display and (e.g., by voice) as follows: if a user pedals at a        ROM radius greater than indicated for their current exercise        level, they will advance to the higher exercise level upon        meeting the following additional requirements:        -   1. improvement in Degree of Pain, and        -   2. during the two subsequent Exercise Sessions demonstrating            the prior Exercise Level is too easy by:            -   (a) using the new pedal radius, and            -   (b) pedalling without pain.

c) Heart Rate Test: Level 1-3

-   -   i. The user attaches the wrist tracker to their arm to begin        their initial heart rate test (cycling). The intensity of the        workout will stay steady for 3-minutes to measure their baseline        heart rate. At the end of the 3 minutes, the processing device        records the user's heart rate.

d) Heart Rate Test: Level 4-5

-   -   i. The user attaches the wrist tracker to their arm for the test        (cycling). When the test begins, the intensity of the workout        slowly increases the user's heart rate reaches the “Test Zone”.        This zone is individually computed to be near 75 percent of the        maximum heart rate for the user's profile. When the user reaches        the Test Zone, the system holds the intensity steady for 3        minutes. At the end of the 3 minutes, the processing device        records the user's heart rate and power output. This data along        with the user's age and weight, are computed to produce a        “Fitness Score” baseline.

e) Fitness Test: Portable Product

-   -   i. Strength Test: Pushup test for motivation by tracking        progress over time, but no impact on protocol design. Frequency        of fitness testing: every 8 weeks

f) Fitness Test: Subsequent Products

-   -   i. Strength Test: Pushup test and use inputs to drive phases—if        the user performs poorly in strength, make the next 8-week phase        focused on strength building, if the user performs poorly or        average on endurance make next 8-week phase focused on building        endurance, etc. Frequency of fitness testing: every 8 weeks

3. Exercise Sessions

a) Composition of each Exercise Level

-   -   i. Levels 1-3 Exercise Sessions        -   1. Cycle: 5-minutes        -   2. Warm-up: 5-minutes +(−) 30 seconds        -   3. Strength Building: 5-minutes +(−) 30 seconds        -   4. Cool Down: 5-minutes +(−) 30 seconds    -   ii. Levels 4-5 Exercise Sessions        -   1. Warm-up: 2-minutes +(−) 30 seconds        -   2. Cardio: 8-minutes +(−) 30 seconds        -   3. Strength Building: 8-minutes +(−) 30 seconds        -   4. Flexibility: 5-minutes +(−) 30 seconds        -   5. Cool Down: 2-minutes +(−) 30 seconds    -   iii. Level 4-5+ Options for exercise selection        -   1. Once users reach full ROM and no pain, an option is            provided to the users of picking their own 8 week programs            or having the system pick the program        -   2. Programs can be based on the result of the user's fitness            test or can be randomized (ideally they will be based on the            user's Fitness Test)        -   3. The system can devise a phased program to address a            deficiency in their fitness test (i.e., user is average or            poor in endurance so the 8 week phase is aimed at improving            endurance)        -   4. The user can choose an 8 week program based on the            deficiency in their fitness test (i.e., the user is alerted            he/she is average or poor in endurance so the user picks 8            week phase is aimed at improving endurance)        -   5. The system can randomly assign an 8 week program        -   6. The user can select any 8 week program        -   7. Programs can also be based on achieving a fitness or            activity of daily living goal.            -   (a) A user selects a goal from a pre-loaded picklist                (this will be selected from market research about common                fitness/activities of daily living goals)            -   (b) Based on the goal selected the system designs an 8                week program that addresses the performance requirement                to achieving that goal.

b) Exercise Filtering:

-   -   i. Exercise Selection:        -   1. First filter exercises by exercise level        -   2. Then filter exercises within each exercise level by            section (warm up, cycling, strength, flexibility, cool down)        -   3. Then filter exercises within each section by “Time to            Complete”            -   (a) Exercise combination should equal +/−30 seconds from                5 minutes per section.        -   ii. Incorporate the user's historical data 1. Block out            exercises completed often (e.g., within a certain time            period, each exercise session, etc.)        -   2. Start new sessions with reps/sets based on previous            session output (e.g., ended with 6 reps so start next            session with 6 reps)    -   iii. Session Counter        -   1. The system will keep a cumulative log of every time a            user logs in and completes a session. After every 5th login            and completion, the next session will begin with a ROM test.

c) Evaluative Sessions

-   -   i. The first two sessions for every user will be evaluative.    -   ii. All exercises during Evaluative Sessions may be Level-1        exercises irrespective of the user's actual Exercise Level as        determined in #2 above.    -   iii. After completing the Evaluative Sessions successfully,        users may receive exercises based upon their exercise level as        rated in #2 above.    -   iv. Every exercise may be announced by the display, virtual        coach, etc. and (e.g., by voice).        -   1. The user may be required to watch the exercise tutorial            the first two times an exercise is presented. Thereafter,            the User will be given the option by the display, virtual            coach, etc. and (e.g., by voice) to watch the tutorial or to            “Skip” it (e.g., by voice) or by making an entry to the            dashboard.

4. Exercise Adaptations

a) When A User Presses or commands (e.g., by voice) The Too Easy Button

-   -   i. The exercise they are then performing may automatically        increase 15% in intensity (rounded up to the nearest whole        number) in one of the following dimensions, according to the        exercise:        -   1. Number of Sets (Increase)        -   2. Number of Repetitions (Increase)        -   3. Hold Time (Increase)        -   4. Rest Time (Reduce)    -   The duration of exercise sessions (3a above) may be extended by        the additional time required for the user to perform exercises        at an increased intensity.    -   ii. Users may press the display button or say (e.g., by voice)        “Too Easy” up to three times per exercise per exercise session.        Each time:    -   1. the User will be alerted on the dashboard and by the virtual        coach to the degree of difficulty increase in repetitions and        sets, and

2. will have the option of pressing or saying (e.g., by voice) “TooHard” on the new level, and the repetitions and sets may immediatelyrevert to their prior values.

-   -   iii. Users may perform the Baseline Exercise for 3 Exercise        Sessions before the “Too Easy” button becomes available to use.

b) When a User Presses Button or Says (e.g., by Voice) Too Hard

-   -   i. The key exercise dimension for non-cycling exercise drops:        -   1. the first time by 20% of the original amount (rounded            down to the nearest whole number), then        -   2. the second time by 40% of the original amount, then        -   3. the third time by 80% of the original amount.    -   ii. The key exercise dimension for cycling drops:        -   1. first by reducing the Resistance Level setting, then        -   2. by reducing the ROM setting.    -   iii. If a user presses button or says (e.g., by voice) “Too        Hard” 3 times on the same exercise in the same session, a push        notification message may appear and ask if everything is alright        and ask if they want to continue with the exercise.    -   iv. If a user presses button or says (e.g., by voice) “Too Hard”        a 4th time, the exercise will stop, and a push notification and        notification may let the user know to stop this exercise for the        session.

c) When A User Presses Button or Says (e.g., by voice) Skip

-   -   i. The system may record the exercise was skipped, and may begin        the exercise at the user's previous exercise level the next time        the exercise is shown.

5. In-Session Exercise Switching

a) When a user has completed the Baseline Version of an exercise 3times, the system may switch out exercises by randomly delivering one offour variations of the same exercise.

-   -   Variation 1—Sets    -   Variation 2—Repetitions    -   Variation 3—Hold Times    -   Variation 4—Rest Time

b) If a user has performed an exercise (including the Baseline &variations) in 15 sessions, the user will be asked through a pushnotification if they would like to swap out the exercise for another.

6. Counting Reps & Sets

a) Exercises may be timed to our rep time per exercise on our exercisedatabase:

-   -   i. This informs the system of sets/reps completed    -   ii. Reps will be calculated by dividing the amount of time the        user completed the exercise by the rep time per exercise        -   1. EX: User does an exercise for 1minute and the rep time            per exercise in the dashboard is 30 seconds—the user            completed 2 reps of that exercise    -   iii. Users will be able to stop an exercise at any point by        pressing “Skip” on the screen or by saying “Skip”.    -   iv. The system determines an exercise is completed in one of two        ways:        -   1. The total time per exercise (as directed by the database)            has elapsed        -   2. A user presses “Next” on the screen or says “Next”.

b) Users have the option of personally reporting reps/sets completed byentering the data on the screen or by saying the number of each aftereach exercise.

7. Completing An Exercise or an Exercise Session

a) System Default: Communicating the completion of both exercises andexercise sessions can be accomplished by using either a button or byvoice.

-   -   i. Completing an Exercise: user pushes “Next” button or says        “Next”.    -   ii. Completing an Exercise Session: user pushes “End Session”        button or says “End Session”.

b) User Choice: Do the exercises continually until pushing the “Stop”button or saying “Stop”.

8. Advancing Out Of Levels

-   -   If a user conducts their ROM test, and pedals at a ROM radius        consistent with a radius higher than their current level:        -   On the user's next 2 subsequent sessions            -   Their cycling portion will be done at the increased                radius.    -   If the user does not indicate an increase in pain when logging        in for those 2 subsequent cycle portions, the user may be        advanced to the new level in their next session.

9. Exercise Session Example For Level-1

a) Warm Up Cycling

-   -   -   5 minutes at the ROM level during setup

b) Exercises

-   -   Squats—Chair rise        -   5 times        -   2 sets        -   30 second rest between each set    -   Calf Raises        -   10-20 times        -   2 sets        -   30 second rest between each set    -   Hamstring Curl        -   Hold for 5 seconds        -   5 times        -   2 sets        -   30 second rest between each set    -   Standing Hip Extension        -   Hold for 5 seconds        -   5 times        -   2 sets        -   30 second rest between each set    -   Standing Hip Abduction        -   Hold for 5 seconds        -   5 times        -   2 sets        -   30 second rest between each set    -   Seated Hip Adduction        -   Hold for 5 seconds        -   5 times        -   2 sets        -   30 second rest between each set

c) Strengthening Cycling

-   -   -   After pedaling for 3 minutes, increase the arc one notch.        -   5-8 minutes, at a comfortable pace, 4-5/10 level

d) Cool Down

-   -   Seated hamstring stretch (end)        -   Hold for 2 seconds        -   10 times per side

Moving on to Levels 4 & 5

-   -   At these levels a user may have full range of motion and their        pain may be almost gone.    -   The next step may include helping them with their fitness goals        (ex: playing with grandkids, golfing, skiing) by adding        resistance, endurance, cardio and loading exercises in a        protocol/program tailored to that goal        Level 4 Session Example—25 mins

Warm Up

-   -   Stretching—5 minutes

Cardio

-   -   Cycling Program: Ride In The Park—5 minutes

Strength

-   -   Band Exercises: Chest Press, Seated Row, Squats—5 minutes

Flexibility

-   -   Goddess, Seated Twist, Stand Hip Adduction—5 minutes

Cool Down

-   -   Seated Marching, Hip Extension, Hamstring Curl—5 minutes        Level 5 Session Example—25 mins

Warm Up

-   -   Stretching—5 minutes

Cardio

-   -   Cycling Program: Pike's Peak—5 minutes

Strength

-   -   Band Exercises: Bicep Curl, Shoulder Press, Pull Apart—5 minutes

Flexibility

-   -   Seated Hamstring Stretch, Hip Abduction, Calf Raise, —5 minutes

Cool Down

-   -   Seated Side Step, Seated Ankle Pumps, Sitting Knee Extensions —5        minutes

Additional Algorithm Rules

Each exercise may target at least one body part. The body part targetsmay be tagged in the data structures as follows:

-   -   whole leg    -   lateral hip    -   medial hip    -   posterior hip    -   hamstrings    -   quadriceps    -   hip flexion

Each session may include at least one exercise that targets each of thebody parts listed.

The exercises per session must not exceed a percentage (e.g., 5, 10, 20,30, 40, 50, etc.) of one particular body part target.

The number of exercises assigned to each body part may be be tabulated.The selection of an exercise for any body part, should not result intotal exercises for that body part exceeding the total of any other bodypart by more than one.

Resistance (Cycling)

Level 1 Level 2 Level 3 Level 4 Level 5 NA NA 1/Easy  2/Easy 6/Medium1/Back/pull  3/Easy 7/Medium 4/Medium  8/Hard 5/Medium 10/Hard

Bands

Level 1 Level 2 Level 3 Level 4 Level 5 1/Mini 1/Mini 1/Mini 1/Mini1/Mini 2/Thin 2/Thin 2/Thin 2/Thin 3/Heavy 3/Heavy

The above discussion is meant to be illustrative of the principles andvarious embodiments of the present disclosure. Numerous variations andmodifications will become apparent to those skilled in the art once theabove disclosure is fully appreciated. It is intended that the followingclaims be interpreted to embrace all such variations and modifications.

What is claimed is:
 1. A computer-implemented method for using anartificial intelligence engine to onboard a user for an exercise plan,wherein the computer-implemented method comprises: generating, by theartificial intelligence engine, a machine learning model trained toreceive as input onboarding data associated with a user and anonboarding protocol and, based on the onboarding data and the onboardingprotocol, output an exercise plan; while a user performs an exerciseusing the exercise device, receiving the onboarding data associated withthe user; determining, by the machine learning model, wherein themachine learning model uses the onboarding data and the onboardingprotocol, a fitness level of the user, wherein: the onboarding protocolcomprises exercises with tiered difficulty levels, the onboardingprotocol increases a difficulty level for a subsequent exercisecomprising the exercises when the user completes an exercise comprisingthe exercises, and based on a completion state of a last exerciseperformed by the user, the fitness level of the user is determined; andby associating the difficulty level for each exercise with the fitnesslevel of the user, selecting a difficulty level for each exercisecomprising the exercise plan.
 2. The computer-implemented method ofclaim 1, wherein the exercises are associated with a plurality ofdomains comprising range of motion, strength, balance, endurance,mobility, stability, pliability, flexibility, or some combinationthereof.
 3. The computer-implemented method of claim 1, furthercomprising: as the user performs the exercise plan, receiving one ormore measurements; based on the one or more measurements, modifying aportion of the exercise plan to include different exercises.
 4. Thecomputer-implemented method of claim 1, further comprising: receivingfeedback from the user, wherein the feedback pertains to a pain level ofthe user, an enjoyment level of the user performing the exercise plan,or some combination thereof; and based on the feedback, modifying theexercise plan to include different exercises.
 5. Thecomputer-implemented method of claim 1, further comprising: presenting avirtual character on a computing device associated with the exercisedevice, wherein the virtual character comprises a coach that providesinstructions as to how to properly perform an exercise.
 6. Thecomputer-implemented method of claim 5, wherein the coach is a virtualavatar.
 7. The computer-implemented method of claim 5, wherein the coachis a live person presented in real-time.
 8. The computer-implementedmethod of claim 5, wherein the coach is a pre-recorded live person. 9.The computer-implemented method of claim 1, wherein the machine learningmodel is trained to generate the exercise plan to reduce a pain level ofthe user, a dependency of the user on a certain medication, or somecombination thereof.
 10. The computer-implemented method of claim 1,further comprising presenting the exercise plan on a computing deviceassociated with the exercise device.
 11. The computer-implemented methodof claim 1, further comprising: receiving input from the user, whereinthe input comprises an instruction to modify an operating parameter ofthe exercise device, and the input is received via a microphone, atouchscreen, a keyboard, a mouse, a haptic signal, or some combinationthereof.
 12. A tangible, non-transitory computer-readable medium storinginstructions that, when executed, cause a processing device to:generate, by an artificial intelligence engine, a machine learning modeltrained to receive as input onboarding data associated with a user andan onboarding protocol and, based on the onboarding data and theonboarding protocol, output an exercise plan; while a user performs anexercise using an exercise device, receive the onboarding dataassociated with the user; determine, by the machine learning model,wherein the machine learning model uses the onboarding data and theonboarding protocol, a fitness level of the user, wherein: theonboarding protocol comprises exercises with tiered difficulty levels,the onboarding protocol increases a difficulty level for a subsequentexercise comprising the exercises when the user completes an exercisecomprising the exercises, and based on a completion state of a lastexercise performed by the user, the fitness level of the user isdetermined; and by associating the difficulty level for each exercisewith the fitness level of the user, select a difficulty level for eachexercise comprising the exercise plan.
 13. The computer-readable mediumof claim 11, wherein the exercises are associated with a plurality ofdomains comprising range of motion, strength, balance, endurance,mobility, stability, pliability, flexibility, or some combinationthereof.
 14. The computer-readable medium of claim 11, wherein theprocessing device is configured to: as the user performs the exerciseplan, receive one or more measurements; based on the one or moremeasurements, modifying a portion of the exercise plan to includedifferent exercises.
 15. The computer-readable medium of claim 11,wherein the processing device is configured to: receive feedback fromthe user, wherein the feedback pertains to a pain level of the user, anenjoyment level of the user performing the exercise plan, or somecombination thereof; and based on the feedback, modify the exercise planto include different exercises.
 16. The computer-readable medium ofclaim 11, wherein the processing device is configured to: present avirtual character on a computing device associated with the exercisedevice, wherein the virtual character comprises a coach that providesinstructions as to how to properly perform an exercise.
 17. Thecomputer-readable medium of claim 16, wherein the coach is a virtualavatar.
 18. The computer-readable medium of claim 16, wherein the coachis a live person presented in real-time.
 19. The computer-implementedmethod of claim 16, wherein the coach is a pre-recorded live person. 20.A system comprising: A memory device storing instructions; A processingdevice communicatively coupled to the memory device, wherein theprocessing device executes the instructions to: generate, by anartificial intelligence engine, a machine learning model trained toreceive as input onboarding data associated with a user and anonboarding protocol and, based on the onboarding data and the onboardingprotocol, output an exercise plan; while a user performs an exerciseusing an exercise device, receive the onboarding data associated withthe user; determine, by the machine learning model, wherein the machinelearning model uses the onboarding data and the onboarding protocol, afitness level of the user, wherein: the onboarding protocol comprisesexercises with tiered difficulty levels, the onboarding protocolincreases a difficulty level for a subsequent exercise comprising theexercises when the user completes an exercise comprising the exercises,and based on a completion state of a last exercise performed by theuser, the fitness level of the user is determined; and by associatingthe difficulty level for each exercise with the fitness level of theuser, select a difficulty level for each exercise comprising theexercise plan.